Uç N. MADHUSUDHANAREDDYM.Sc.,

Transcription

1 DESIGN AND DEVELOPMENT OF ETHERNET BASED DC MOTOR SPEED CONTROL SYSTEM USING ARM Cortex PROCESSOR AND ITS APPLICATION FOR SOLAR POWER TRACKING Thesis submitted to SRI KRISHNADEVARAYA UNIVERSITY in partial fulfilment for the award of the degree of DOCTOR OF PHILOSOPHY in ELECTRONICS Uç N. MADHUSUDHANAREDDYM.Sc., hçwxü àx exáxtüv fâñxüä á ÉÇ Éy Prof. K. NAGABHUSHAN RAJU M.Tech., Ph.D. DEPARTMENT OF INSTRUMENTATION & USIC SRI KRISHNADEVARAYA UNIVERSITY ANANTAPUR , (A.P.), INDIA MARCH, 2013

2 Prof. K. Nagabhushan Raju Department of Instrumentation Sri Krishnadevaraya University Anantapur , A.P. Office : Res. : VxÜà y vtàx This is to certify that the thesis entitled DESIGN AND DEVELOPMENT OF ETHERNETT BASED DC MOTOR SPEED CONTROL SYSTEM USING ARM Cortex PROCESSOR AND ITS APPLICATIONS FOR SOLAR POWER TRACKING submitted by N. MADHUSUDHANA REDDY for the award of Doctor of Philosophy in Electronics, to the Sri Krishnadevaraya University, Anantapur, is a record of bonafide research work carried out by him during the period of his study in this University under my guidance and supervision and that no part of thesis has previously formed the basis for the award of any degree, diploma, associateship, fellowship or other similar titles. Place: Anantapur Date: (K. NAGABHU USHAN RAJU) Research Supervisor

3 DECLARATION I hereby declare that the research work presented in this thesis entitled Design and development of Ethernet based DC motor speed control system using ARM Cortex processor and its applications for solar power tracking is an original work carried out by me, under the supervision of Prof. K. Nagabhushan Raju, Department of Instrumentation, Sri Krishnadevaraya University, Anantapur for the partial fulfilment of the requirement for the award of the degree of DOCTOR of PHILOSOPHY in ELECTRONICS. This research work has not been submitted to any other university for the award of any degree or diploma. Date: Place: Anantapur N. MADHUSUDHANA REDDY

4 Dedicated To and My Family

5 ACKNOWLEDGEMENTS This dissertation is the result of several years of research at the Sri Krishnadevaraya University, Anantapur and I feel deeply indebted to a number of people who directly or indirectly inspired its realization. First, I would like to thank my research supervisor Prof. K. Nagabhushan Raju for his precious help and support during these years. My gratitude also goes to him for his technical guidance, and for his enthusiastic encouragement. I learned from him scientific thinking and admire his sincerity, patience and dedication. I am grateful to him for his advice during its elaboration. I express my deep sense of gratitude to Prof. B. Ramamurthy, Coordinator, Department of Electronics, Sri Krishnadevaraya University, Anantapur for his constant encouragement and support throughout my research work. I shall ever remain grateful and indebted to my chief mentor and beloved person Dr. K. Suvarna, Professor, Department of Mathematics, Sri Krishnadevaraya University. She proved to be a friend, philosopher and guide in veridical sense of term and stood by me through the thick and thin of my research regime. I am and will forever remain duty-bound to her kind co-operation, encouragement and help extended that went even beyond the academics. She is a constant source of inspiration to me in all my endeavors. These words cannot express my gratitude to her.

6 Many thanks go to engineer D. Chandrashekar Reddy, Megabytes Technologies, Bangalore who helped me in this research journey. I also thank my fellow researchers and friends in the Department of Electronics and Instrumentation for their help and company during my small research era. Thanks to Wipro Technologies, Bangalore for their kind support in the completion of this research work. I am also obliged to the authorities of Sri Krishnadevaraya University, Anantapur for providing me all the facilities to carryout research studies. Finally, I would like to express my deepest gratitude to my family, for their support, and in particular to my wife for her repeated encouragement throughout the preparation of this dissertation. Many friends also supported me during this period and I wish to thank them all individually. N. Madhusudhana Reddy.

7 CONTENTS Preface i - v CHAPTER 1: INTRODUCTION TO CONTROL SYSTEMS Process Control System Servo Control System ON/OFF Control Proportional Control [P] Integral Control [I] Proportional plus Integral Control [PI] Proportional plus Derivative control action [PD] Proportional plus Integral plus Derivative 22 Controller (PID) control 1.9 Ziegler Nicholas (ZN) method 27 References 31 CHAPTER 2: REVIEW OF LITERATURE General Literature Survey P, PI and PID control DC motor speed control and its applications Solar tracking and Control unit Motivation for present work 73 References 75 CHAPTER 3: OVERVIEW OF EMBEDDED SYSTEMS Characteristics Challenges and Recent trends in embedded systems ARM Processor Technology 102 References 132

8 CHAPTER 4: DESIGN AND DEVELOPMENT OF ETHERNET BASED D.C MOTOR SPEED CONTROL BY USING ARM Cortex PROCESSOR 4.1 Introduction Principle Hardware features Software Features Experimental implementation Flow chart of the PID Results and Discussion 162 References 167 CHAPTER 5: APPLICATION OF DC MOTOR SPEED CONTROL FOR SOLAR POWER TRACKING SYSTEM 5.1 Introduction Principle Hardware features Experimental Implementation Software features Results and discussion 191 References 197 CHAPTER 6: CONCLUSIONS AND FUTURE SCOPE Conclusions Future Scope for Present Work 204 APPENDIX PUBLICATIONS 283

9 PREFACE In the process control field, majority of control loop theories deal with the control of simple process techniques. In practical process control applications such as speed and position controlling of the motors and many complex processes, the mathematical model construction is impossible because of its time varying and non-linearity. In such processes optimal and effective operation controller techniques are economically vital for process industries. The algorithm based on Proportional-Integral-Derivative (PID) controllers is one of such techniques. The basic structure of the PID controllers makes it easy to regulate the process output, and design methods leading to an optimal and effective operation of the PID controllers. Robust control has been a recent addition to the field of control engineering that primarily deals with obtaining system robustness in the presence of uncertainties. In the present thesis, a graphical design method for obtaining the entire range of PID controller gains that robustly stabilize a system in the presence of time delays and additive uncertainty is introduced. This design method primarily depends on the frequency response of the system, which can serve to reduce the complexities involved in plant modeling. The need for efficient, accurate and simple advanced alternatives arises especially in the embedded development for process control applications, where most of the real processes are generally complex, time variant, nonlinear, partially known and difficult to model. The usage of ARM controllers in wide range of control applications has made possible establishment of intelligent control in these areas. i

10 During the past several years, ARM based controllers have emerged as one of the most active and fruitful controllers for research application development and control algorithms implementation. ARM controllers are frequently used for various applications, as they consume low power and allow accurate, efficient, and cost effective implementation. It offers a rigorous and practical technique for manipulating the qualitative results originally expected. In the present study, Universal Asynchronous Receivers/Transmitters (UART), Pulse Width Modulator (PWM), Analog-to-Digital Converter (ADC) and Ethernet features of the controller are enabled by calling the corresponding Application Programming Interface (API) system calls. Control signals are received from the remote computer through Ethernet channel of the controller and PWM signals are generated to control the speed of the DC motor. The speed of the motor is measured using F/V converter, whose output is connected to the controller. The inbuilt 12 bit A/D converter provides digital equivalent of the voltage, and after that the samples are sent to personal computer (PC) through Ethernet communication. The Ethernet data transmission has become very important to the overall computing strategy in industrial and commercial applications. In computer, the sample values are read by implementing TCP/IP sockets in C# programming language in ASP.Net. The samples are stored in MS-Access data base file, using file management library functions in.net frame work in PC. Plots are also drawn with the same signals for user flexibility, easy understanding and observation. The speed of the DC motor is measured and compared with given input (investigated) on Personal Computer in the form of graphical representation. The performance of the system is tested with various input signals. ii

11 ARM Cortex processor is having PWM, JTAG, Ethernet and ADC as inbuilt features, which will be enabled by calling corresponding API system calls and the pulse width is controlled by setting the on-off time. Pulse-width modulation (PWM) or pulse-duration modulation (PDM), is a commonly used technique for controlling power to internal electrical devices. Control signals are emanated from the remote computer to control the speed of DC motor. In this study, pulse width modulation (PWM) technique is initiated by calling API system calls. This basically involves taking a constant DC supply voltage and chopping it so that the average value is varied. Hence PWM is used to control the average voltage applied to the armature. PWM wave is an analog signal that switches between two predefined limits. The switching interval of the PWM, controlled by the reference signal, determines the average power delivered to the motor through actuator. By varying the duty cycle of the PWM the speed of the motor is controlled. Joint Test Application Group (JTAG) is used to debug the application program and to burn the.exe in to the controller PROM/EEPROM/SDROM. It is used to debug the application programming code statement by statement, by setting the set points and break points at different routines to track software bugs and hardware response signals and to monitor processor register status and contents. Ethernet technique is used to establish communication with remote computer. It is implemented for receiving the control commands from PC to motor to control and send back the speed of the motor to personal computer for users to visualize. The control architecture is implemented with the PID logic to control motor speed, due to its excellent speed control characteristics. The PID family iii

12 controllers are successfully applied to many practical process control applications involving the physical parameters like liquid level, liquid flow, pressure, temperature, rotational speed of motor etc. The methodology encompasses the design of PID controllers for D.C motor controlling with step response and provides the means for a systematic adjustment of the controller gain in order to meet transient performance specifications. The present thesis describes the design, development, fabrication, and analysis of ARM processor based PID logic controller for DC motor speed control systems and applying the same for solar tracking using Ethernet. The work presented in the thesis is divided into six chapters, each chapter being sub divided into different sections. The first chapter gives an overview on origin and evolution of P, PI, and PID theory, introduction to control system and process control system theory, servo control system, ON/OFF control theory. The control strategies of Proportional [P] control action, Proportional plus Integral [PI] logic control and Proportional plus Integral and Derivative logic control [PID] are discussed in detail. It also discusses the Zeiglar-Nichols method, optimal and non-optimal methods. A brief review of comparisons of the on/off, P, PI, and PID controls are presented and references are mentioned at the end of the chapter. The second chapter reviews and discusses the general literature survey, literature related ARM processors, methods and applications of P, PI, and PID Logic Controllers for Process parameters in Control System and solar trackers. This chapter also discusses the motivation for the present work. iv

13 The third chapter describes an overview of embedded systems. Recent trends in embedded systems, details of ARM Cortex based Microcontroller LM3S9B96 which includes the introduction of microcontrollers, architecture, 12-bit Analog to digital converter, PWM generator, general purpose of Input/output ports and Ethernet communication details. Chapter four deals with design and development of Ethernet based DC Motor speed control by using ARM Processor. This chapter discusses the principle, hardware, and software features. The hardware includes the PMDC motor and speed sensing unit, F/V converter, Personal Computer, driver circuit and final control element. The experimental implementation of PID is discussed. The software includes PID, flowcharts for embedded C language program implementation. The experimental results, relevant interpretations for these results and conclusions are also discussed. Chapter five deals with design and development of ARM-Cortex based solar tracking system using DC motor for maximum power. This chapter discusses the principle, hardware, and software features. The hardware includes the DC motor and solar panel, sensor circuit, Personal Computer, relay circuit and final control element. The experimental implementation of solar tracking system is discussed. The software includes flowcharts for embedded C language program implementation. The experimental results, relevant interpretations for these results and conclusions are also discussed. Chapter six presents the experimental results and important conclusions of the present study and scope for the future work in this area. The source code in embedded C for the present work is given in Appendix. v

14 Chapter 1 INTRODUCTION TO CONTROL SYSTEMS

15 A control system consisting of interconnected components is designed to target a desired purpose. It is useful to examine examples of control systems through the course of history. These early systems incorporated many of the same ideas of feedback that are in use today. A control system is a device or set of devices to manage, command, direct or regulate the behavior of other devices or systems. Closed loop control systems are used in many industrial process applications for controlling the speed of motors. Simple control loops include a set of points or desired value input, a measurement input, which indicates the actual value of the parameter to be controlled, and a comparator to develop an error signal related to the difference between the desired and actual values. A control loop output signal related to the error signal is then applied to the control device whose parameter is to be controlled by the loop. The control accuracy and response characteristics of control loops are conventionally enhanced by adding various control terms or weightings to the error signal in order to develop the control output signal. One classic enhanced servo control loop is known as the PID loop, which includes proportional integral and derivative terms added to the error signal to develop the desired control signal.pid loops are often applied where the accurate maintenance of controlled parameter is 1

16 important, such as the control of the rotational speed of the capstan in a magnetic tape drive [1, 2]. The first significant work in automatic control was James Watt s centrifugal governor for the speed control of a steam engine in the eighteenth century. Other significant works in the early stage of development of control theory were due to Minorsky, Hazen and Nyquist [3] among many others. In 1922 Minorsky [3] worked on automation controllers for steering ship and showed how stability could be determined from the differential equation describing the system. In 1932 Nyquist [3] developed a relatively simple procedure for determining the stability of closed loop systems on the basis of open-loop response to steady-state sinusoidal inputs. In 1934 Hazen [3] introduced the term servomechanisms for speed and position control system. During the decade of the 1940 s frequency response methods made it possible for engineers to design linear feedback control system that satisfied performance requirement. From the end of the 1940 s to early 1950 s the root locus method in control system design was fully developed. As modern plants with many inputs and outputs became more and more complex, the description of modern control system required a large number of equations. Classical control theory, which deals only with single input single output system, became entirely powerless for multiple input multiple output system. Since about 1960, modern control theory has been developed to cope with the increased complexity of modern plants and the stringent requirement on accuracy, weight, and cost in military, space, and industrial applications. 2

17 The art of automatic control dominated the modern way of life and can be used either in ensuring peace or destruction of the world. Every gadget like automatic toaster, temperature control of a room, water level control in an overhead tank etc., have influenced the current way of life to a large extent and have made human being to enjoy more and more comforts. Also the control systems used in anti-aircraft gun control, guided missiles and other weapons using nuclear energy can cause destruction of mankind. The fly ball governor for controlling the speed suggested by James watt in 1788 can be thought of as the first feedback system not involving a human being. After this, many research publications appeared on the same and similar control systems [4]. Automatic control has played a vital role in the advancement of engineering and science. In addition to its importance in space-vehicles, missile guidance, and aircraft-piloting systems, automatic control has become an important and integral part of modern manufacturing and industrial processes. For example, automatic control is essential in such industrial operations as controlling pressure, temperature, humidity, viscosity and flow in the process industries, tooling, handling and assembling mechanical parts in the manufacturing industries among many others. An autonomous mobile robot central control is regulated by an Industrial Process Control [IPC], which controls every function of security, steering, positioning localization and driving. Each traction wheel is operated by a DC motor with independent control system [5]. The conventional proportional control term is a linear gain factor related to the difference between the magnitude of the error signal and magnitude of the control signal necessary to achieve the desired result. The conventional integral term is a 3

18 long time constant linear gain term, related to the integral of error signal used to reduce the residual error. Conventional derivative terms, related to the derivative of the error signal and added to enhance system response to such short-term transient without reducing the long term accuracy benefits of the integral terms. PID control applied in a software implementation by an appropriate algorithm is used to generate a value for the control signal in response to applied values for the measurement and set point inputs. Control systems are classified into two general categories open loop and closed loop control systems. Open loop control systems are control systems in which the output has no effect upon the control action. In an open-loop control system, the output is neither measured nor fed back for comparison with the input. For example, in a washing machine, soaking, washing, and rinsing are operated on a time basis. The machine does not measure the output signal namely the cleanliness of clothes. In any open-loop control system the output is not compared with the reference input. Hence, for each reference input, there corresponds a fixed operation condition. Thus, the accuracy of the system depends on the calibration. In the presence of disturbance, an open-loop control system will not confirm the desired task. Open-loop control can be used in practice only if the relationship between the input and output is known and if there are neither internal nor external disturbances. Clearly such systems are not feedback control systems. Any control system, which operates on a time 4

19 basis, is open loop. The block diagram of the open loop control system is shown in Figure 1.1. Reference Controller U Process Controlled Output U = Actuating Signal Figure1.1 Block diagram of open loop control system Advantages of open loop control systems are simple construction, very much convenient when output is difficult to measure, and such systems are easy from maintenance point of view. Generally, these are free from the problems of stability. Such systems are simple to design, economical and give inaccurate results if there are variations in the external environment i.e. they cannot sense environmental changes. They are inaccurate and unreliable because accuracy of such system is totally dependent on the accurate pre-calibration of the controller. Similarly they cannot sense internal disturbances in the system, after the controller stage. To maintain the quality and accuracy, recalibration of the controller is necessary from time-to-time. To overcome all the above disadvantages, generally closed loop systems are used in practice. Closed loop control system is one in which the output signal has a direct effect upon the control action. That is, a closed-loop control system incorporates 5

20 feedback element. The actuating error signal, which is the difference between the input signal and the feedback signal (which may be the output signal or function of the output signal and its derivatives), is fed to the controller so as to reduce the error. In other words, the term closed loop implies the use of feedback action in order to reduce the system error. The error signal produced in the automatic controller is amplified and the output of the controller is sent to the control value in order to change the valve opening for steam supply so as to correct the actual water temperature. If there is no error, no change in the valve operation is necessary. The control of a complex system by a human operation is not effective because of the many interrelations among various variables. Automatic control systems eliminate any human error in operation. If high precision control is necessary, control must be automatic. The block diagram of the closed loop control system is shown in Figure 1.2. c(t) Command Input Reference transducer r(t) ref. input e(t) + b(t) Controller m(t) Process to be controlled Feedback element Figure 1.2: Block diagram of the closed loop system c(t) = controlled output b(t) = feedback signal e(t) = error signal m(t) = manipulated signal r(t) = reference input 6

21 System in which the controlling action or input is somehow dependent on the output or changes in output are called closed loop system. Feedback is a property of the system by which it permits the output to be compared with the reference input so that appropriate controlling action can be decided. In such a system, output or part of the output fed back to the input for comparison with the reference input applied to it. It is not possible in all the systems that available signal can be applied as input to the system. Depending upon the nature of controller it is required to reduce it or amplify to change its nature. This changed input as per requirement is called reference input, which is to be generated by using reference transducer. The main excitation to make the system called its command input, which is then applied to the reference transducer to generate reference input. The output, which is to be decided by feedback element, is fed back to the reference input. The signal, which is output of feedback element, is called feedback signal b(t). It is then compared with the reference input giving error signal e(t)=r(t)+b(t). When feedback signal is positive it is called positive feedback system and if the signal is negative it is called negative feedback system. This modified error signal then actuates the actual system and produces the controlled output c(t). An advantage of closed loop control system is that the use of feedback makes the system response relatively insensitive to external disturbances and internal variations in the system parameters. It is thus possible to use relatively inaccurate and inexpensive components to obtain the accurate control of a given plant, whereas this is impossible in the open-loop case. From the point of ability, the open-loop control system is easier to build since stability is not a major problem. 7

22 On the other hand, stability is always a major problem in the closed-loop control system since it may tend to over correct errors, which may cause oscillations of constant or changing amplitude. It should be emphasized that for systems in which the inputs are known ahead of time and in which there are no disturbances, it is advisable to use open-loop control. Closed-loop control systems have advantages only when unpredictable disturbances and/or unpredictable variations in system components are present. A proper combination of open-loop and closed-loop control is usually less expensive and satisfies the overall system performance. The design and implementation of smart structural systems necessitates the integration of mechanical systems with sensors, actuators, and control systems for higher performance and self-diagnosis capabilities. A key element of this combination is the integration of the control system into the structure [6]. Control is important for most industrial processes to the avoid disturbances which degrade the overall process performance, and a great deal of work is being done in this field [7]. The nice thing about tuning a PID controller is that the user need not have a good understanding of formal control theory to do a fairly good job of it. About 90% of the closed-loop control applications in the world do very well indeed with a controller that is only tuned fairly well. 1.1 Process Control System The process control system is the entity that is charged with the responsibility for monitoring outputs, making decisions about how best to manipulate inputs so as to obtain desired output behavior, and effectively implement such decisions on the process [8]. The process has a property called self-regulation. A self- regulating 8

23 system does not provide regulation of a variable to any particular reference value. In process control, the basic objective is to regulate the value of some quantity. To regulate means to maintain that quantity at some desired value regardless of external influences. The desired value is called the reference value or set point. In many industrial process control systems, the control process is complex in mechanism, and varying with time. So, general PID control is very difficult to obtain satisfactory effects because it is not self-adaptive for many varying factors such as parameter varying. The process dynamics are concerned with analyzing the dynamic (i.e, time dependent) behavior of a process in response to various types of inputs. In other words, it is the behavior of a process as time progresses [9, 10]. A process is a progressively continuing operation that concedes of a series of controlled actions or movements systematically directed towards a particular result or end. When the automatic control is applied to system, which is designed to regulate the value of some variable to a set point, it is called process control. Examples are chemical, economic, and biological processes. An automatic regulation system in which the output is a variable such as temperature, pressure, flow, liquid level, or ph is called a process control system. Process control is widely applied in industry. Programmed control such as the temperature control of heating furnaces in which the furnace temperature is controlled according to preset program is often used in such systems. For example, a preset program may be such that the furnace temperature is raised to a 9

24 given temperature in some given time interval and then lower to another given temperature in some other given time interval. In such program control, the set point is varied according to the present time schedule. The controller then functions to maintain the furnace temperature close to the varying set point. It should be noted that most process control systems include servomechanism as an integral part [11]. 1.2 Servo Control System A servomechanism is a feedback control system in which the output is some mechanical position, velocity or acceleration. Servo mechanisms are extensively used in modern industry. For example, the completely automatic operation of machine tools, together with programmed instruction, may be accomplished by use of servomechanism. The specialized feedback control system called a servomechanism deserves special attention due to its prevalence in industrial application and control system literature. A servomechanism is a poweramplifying feedback control system in which the controlled variable is mechanical position, or a time derivative of position such as velocity or acceleration. A simple example of servomechanism is a position control system. Consider a load which requires a constant position in this application. The position is sensed and converted to voltage using feedback potentiometer. It is compared with input potentiometer voltage to generate error signal. This is amplified and given to the controller. The controller in turn controls the voltage given to motor, due to which it changes its position. In the servomechanism the objective is to force some 10

25 parameter to vary in a specific manner. This may be called a tracking control system. Instead of regulating a variable value to a set point, the servo mechanism forces the controlled variable value to follow variation of the reference value. For example, in robot arm, servomechanism forces the robot arm to follow a path from one point to another point. This is done by controlling the speed of the motor driving arm and the angles of the arm parts. Advances in servo mechanism have led to the development of the new field of automatic control, the robot and robotology. A robot is a mechanism devised to perform repetitive tasks that are tiresome for a human being or tasks to be performed in a hazardous environment say in a radioactive area. Robots are varying as the tasks that can be imagined to be performed by them. Great strides are being made in this field with the explosion in the power of digital computer; interfacing and software tools which have brought to reality the application of vision and artificial intelligence for devising more versatile robots and increased applications in industrial automation. In fact replacing a human being for a repetitive and/or hazardous task, robots can perform at a greater speed and higher precision. For flexible manufacturing unit s mobile automation have been devised and implemented which are capable of avoiding objects while travelling through a room or industrial plant [12]. An automobile power-steering apparatus is a servomechanism. The command input is the angular position of the steering wheel. A small rotation torque applied to the steering wheel is amplified hydraulically, resulting in a force adequate to modify the output, the angular position of the front wheels. Negative feedback is necessary in order to return the 11

26 control valve to the neutral position, reducing the torque from hydraulic amplifier to zero, when the desired wheel position has been achieved [13-14]. 1.3 ON/OFF Control The most elementary controller mode is the ON/OFF or two-position mode. The ON/OFF controller is the simplest controller that turns the actuator either hard ON or fully OFF. Generally, the two-position control mode is best adapted to largescale systems with relatively slow process rates. Thus, in the example of either a room heating of air-conditioning system, the capacity of the system is very large in terms of air volume, and the overall effect of the heater or cooler is relatively slow. Sudden, large-scale changes are not common to such systems. Other examples of two-position control applications are liquid bath temperature control and level control in large-volume tanks. The process under two-position control must allow continued oscillation in the controlled variable because, by its very nature, this mode of control always produces such oscillations. When it is more than the set point, the controller output is zero. To prevent excessive cycling or chattering, a dead band (hysteresis) is usually introduced to provide finer and smoother control. One of the most elementary types of digital processing has been in use for many years, long before the advent of computers. This is called ON/OFF control because the final control element has only two states, on and off. Thus, the controller output can have only these two states as well. It can be said that the controller output is a digital representation of a single binary digit 0 or 1. Our 12

27 home auto heaters, air conditioners, water heaters, and a host of other basic control systems work according to the same ON/OFF mode. This is the simplest form of control, used by almost all domestic thermostats. When the oven is cooler than the set-point temperature, the heater is turned on at maximum power, and once the oven is hotter than set-point temperature, the heater is switched off completely. The turn-on and turn-off temperatures are deliberately made to differ by a small amount to prevent noise from switching the heater rapidly and unnecessarily when the temperature is near the set point. 1.4 Proportional Control [P] In an analog system, a proportional control system amplifies the error signal to generate the control signal. If the error signal is the voltage, and the control signal is also a voltage, then a proportional controller is just an amplifier. In a digital control system, a proportional control system computes the error from measured output and user input to a program, and multiplies the error by a proportional constant, then generates an output control signal from that multiplication. Proportional control is the easiest feedback control to implement and probably the most common kind of control loop. A proportional controller is just the error signal multiplied by a constant and fed out to the drive. The action means that the controller moves in proportion to the error between set point (SP) and process output (PV): 13

28 Controller output = Kp*error = Kp*(SP - PV) Where the gain is denoted by the parameter Kp. Different manufacturers have used many terms like proportional gain, gain, throttling band, sensitivity and proportional band to designate this action. For small gains (kp = 1) the motor goes to the correct target, but it does so quite slowly. Increasing the gain (kp = 2) speeds up the response to a point. Beyond that point (kp = 5, kp = 10) the motor starts out faster, but it overshoots the target. In the end the system doesn t settle out any quicker than it would have with lower gain, but there is more overshoot. If we kept increasing the gain we would eventually reach a point where the system just oscillated around the target and never settled out and the system would be unstable. The motor and gear start to overshoot with high gains because of the delay in the motor response. Plants that have too much delay, like the precision actuator, can t be stabilized with proportional control. Some plants, like the temperature controller, cannot be brought to the desired set point. Plants like the motor and gear combination may work, but they may need to be driven faster than is possible with professional control alone. To solve these control problems an integral or differential control or both need to be added. A proportional control system is a type of linear feedback control system. The proportional control system is more complex than an on-off control system likes a thermostat, but simpler than proportional-integral-derivative (PID) control system used in something like an automobile cruise control. An on-off control is like driving a car by applying either full power or no power and varying the duty 14

29 cycle, to control speed. The power would be on until the target speed is reached, and then the power would be removed, so the car reduces speed. When the speed falls below the target, with a certain hysteresis, full power would again be applied. It can be seen that this looks like pulse-width modulation, but would result in poor control. A proportional controller attempts to perform better than ON/OFF type by applying power W to the heater in proportion to the difference in temperature between the oven and the set point, W=P*(Ts-To) where P is known as the proportional gain of the controller. As its gain is increased, the system responds faster to changes in set point but becomes progressively under damped and eventually unstable. The final oven temperature lies below the set point for this system because some difference is required to keep the heater supplying power. The heater power must always lie between zero and the maximum because it can only source not sink heat. The proportional (P) controller is an improved controller over ON/OFF controller, wherein the dead band is replaced by proportional band. Over this operational band, the output of the proportional controller varies linearly with the error around zero. Although capable of providing tight control than the ON/OFF controller, the proportional controller cannot fully eliminate the error to cause perfect steady state tracking between the set point and process variable. The controllers are designed to eliminate the need for continuous operator attention. A controller is one, which compares the output value (process variable) with the desired value (set point), determines the error and accordingly produces control action to 15

30 minimize the error. Hence, the controllers are used to automatically adjust some variable to hold the measurement (process variable) at the desired level [15, 16]. The proportional controller produces an output signal which is proportional to error signal. The transfer function of Proportional controller is K p. The term K p is called the gain of the controller. Hence the Proportional controller amplifies the error signal and increases the loop gain of the system. If increased, the loop gain then improves the steady state tracking accuracy, disturbance signal rejection, and stability. It produces a constant steady state error. To handle the immediate error, the error is multiplied by a constant Proportional (P). If the error is zero, a Proportional controller s output is zero. P=Kpe(t) In the proportional control action, the relationship between the output of the controller m(t) and the actuating error signal e(t) is m(t)= Kpe(t) or M(s)/E(s)=Kp E(s) : Error, M(s) : Control K p is termed the actual mechanism, whatever may be the form of the operating power, the proportional controller is essentially an amplifier with an adjustable gain. The block diagram of the Proportional control system is shown at Figure

31 + _ E(s) Kp M(s) Figure 1.3: Block diagram of Proportional Control System 1.5 Integral Control [I] Integral control is used to add long-term precision to a control loop. It is almost always used in conjunction with proportional control. Integral control by itself usually decreases stability, or destroys it altogether. The system doesn t settle. Like the precision actuator with proportional control, the motor and gear system with integral control alone will oscillate with bigger and bigger swings until something hits a limit. This system takes a lot longer to settle out than the same plant with proportional control, but when it does settle out to the target value even with the disturbance added in. If the problem at hand doesn t require fast settling, this might be a workable system. The easiest and most direct way to deal with integrator windup is to limit the integrator state. To learn from the past, the error is integrated and multiplied by a constant I. Without integral term, a controller cannot eliminate error if the process requires a non-null input to produce the desired set-point; integral of the error constantly increases with time. 17

32 I = 1/Ti e(t) dt In a controller with integral control action, the value of the controller output m(s) is changed at a rate proportional to the actuating error signal e(t). m(t) = Ki e(t) dt Where Ki is an adjustable constant. The transfer function of the integral controller is M(s)/E(s) = Ki/s If the value of e(t) is doubled, then the value of m(t) varies twice as fast. For zero actuating error, the value of m(t) remains stationary. The integral control action is sometimes called reset control. 1.6 Proportional plus Integral Control [PI] A controller in the forward path, which changes the controller input to the Proportional plus Integral of the error signal is called PI controller. Sometimes, particular when the sensor measuring the oven temperature is susceptible to noise or other electrical interference, derivative action can cause the heater power to fluctuate wildly. In these circumstances it is often sensible to use a PI controller or set the derivative action of a PID controller to zero. Although capable of providing tight control than the ON/OFF controller, the proportional controller cannot fully eliminate the error to cause perfect steady state tracking between the set point and process variable. An integrator must be added to the proportional controller so as to become proportional plus integral controller. This Proportional pus Integral (PI) controller will provide good steady control, but responds sluggishly to transients. 18

33 The proportional plus integral action reduces the steady state error. The proportional control produces the constant steady state error and integral control reduces the error to zero. A simple combination of the proportional and integral logic provides the proportional integral mode of controller action. The control of a proportional plus integral controller is defined by the following equation m(t) = Kpe(t)+Kp/Ti e(t) dt or M(s)/E(s) =Kp(1+1/Tis) Where Kp represents the proportional sensitivity or gain, and Ti represents the integral time. Both Kp and Ti are adjustable. The integral time adjusts the integral control action, while a change in the value of Kp affects both proportional and integral parts of the control action. The inverse of the integral time Ti is called the reset rate. The reset rate is the number of times per minute that the proportional part of the control action is duplicated. Reset rate is measured in terms of repeats per minute. If the actuating error signal e(t) is a unit-step function as shown Figure 1.4, then the controller output is m(t). R(s) e(t) K + m(t) Plant plus Controller C(s) + Ki/S Figure 1.4: Block diagram of PI control system 19

34 PI controller has the following effects: 1. It increases the order of system. 2. Design of Ki must be proper to maintain stability of the system. So it makes system relatively less stable. 3. Steady state error reduces tremendously for some type of input i.e., in general this controller improves steady state part affecting the transient part. 1.7 Proportional plus Derivate control action (PD) A controller in the forward path, which changes the controller input to the proportional plus derivative of error signal is called PD controller. The precision actuator cannot be stabilized with PI control. The proportional control deals with the present behavior of the plant and that integral control deals with the past behavior of the plant. If we had some element that predicts the plant behavior then this might be used to stabilize the plant. The stability and overshoot problems that arise when a proportional control is used adding a term proportional to the timederivative of the error signal can mitigate high gain, which is known as PD control. W = p*(ts-to) +D*d/dt(Ts-To) The value of the damping constant, D, can be adjusted to achieve a critically damped response to change in the set-point temperature. Too little damping results in overshoot and ringing, too much causes an unnecessarily slow response. 20

35 The proportional plus derivate controller produces an output signal consisting of two-term one proportional to error signal and the other proportional to the derivative of the error signal. The transformation controller is PD = Kp(1+Tds) Where Kp is proportional gain and Td is derivative time. m(t) = Kp e(t) + KpTd de(t)/dt and transfer function is M(s)/E(s)=Kp(1+Tds) Both Kp and Td are adjustable. The derivative control action, sometimes called rate control, is where the magnitude of the controller output is proportional to the rate of change of the actuating error signal. The derivative time Td is the time interval by which the rate action advanced the effect of the proportional-plus derivative controller. If the actuating error signal e(t) is a unit-ramp function, then the controller output is m(t). While derivative control action has an advantage of being anticipatory, it has the disadvantage that it amplifies noise signal and may cause a saturation effect in the actuator. The derivative control action can never be used alone because this control action is effective only during transient periods. The block diagram of PD control system is shown in Figure

36 R(s) K + Plant plus Controller C(s) std + Figure 1.5 Block diagram of PD control system As there are no changes in coefficients, error also will remain the same. Hence PD controller has the following effects on system 1. It increases the damping ratio. 2. It reduces the peak overshoot. 3. It reduces the settling time. 4. Steady state remains unchanged. In general, it improves transient part without affecting steady state. In closed-loop transfer function, it is observed that the PD controller introduces a zero in the system and increases the damping ratio. The addition of the zero may increase the peak overshoot and reduce the raise time. But the effect of increased damping ultimately reduces the peak overshoot. 1.8 Proportional Plus Integral Plus Derivative Controller (PID) As PD improves transient and PI improves steady state, combination of the two may be used to improve overall time response of the system. While PD control 22

37 deals neatly with the over shoot and ringing problems associated with proportional control, it does not cure the problem with the steady-state error. Fortunately it is possible to eliminate this while using relatively low gain by adding an integral term to the control function. The integral gain parameter is sometimes known as the controller reset level. This form of function is known as proportion-integraldifferential or PID control. The effect of integral term is to change the signal power until the time-averaged value of the error is zero. The method works quite well but complicates the mathematical analysis slightly because the system is of third order. PID controllers are most commonly used to regulate the time-domain behavior of many different types of dynamic plants. These controllers are extremely popular because they can usually provide good closed-loop response characteristics. They can be tuned using relatively simple design rules, and are to construct using either analog or digital components [17, 18]. This deficiency can be overcome by the addition of a derivative element, which constitutes a complete PID controller. This gives good transient as well as steady state control. It offers rapid proportional response to error, while having an automatic reset from the integral part to eliminate residual error. The derivative section stabilizes the controller and allows it to respond the rapid changes or transients in error [19]. As the PID controller is composed of three components, it produces an output signal consisting of three term-one is proportional to the error signal e(t), another 23

38 is proportional to integral of error signal e(t) and the third one is proportional to derivative of the error signal e(t) [20-22]. The equation of the PID controller is given as u (t) [e(t)+ e(t) + de(t)/dt] u(t) = K p e(t) + K p / Ti e(t) + K p Td de(t)/dt where, K p is the proportional gain Ti is the integral time Td is the derivative time The transfer function can be written as, U (s)/e(s) = Kp (1+1/Ti + Tds) The block diagram representation of the PID controller is shown in Figure 1.6 and block diagram of the PID based control system is depicted in Figure 1.7 Reference Input r(t) + _ e(t) PID Kp Kp Ti Kpe(t) Kp/Tie(t) Kpe(t)(1+Kp/Ti+KpTd) b(t) Feedback from Output Kp Td KpTde(t) Figure 1.6 Block diagram representation of the PID controller 24

39 PID Controller Set-point r(t) e(t) Kp(1+1/Ti+Td) + _ u(t) PLANT Controller variable c(t) Measured Value b(t) SENSOR Figure 1.7: Block diagram of the PID based control system To ensure the digital implementation of the PID control, the differential equation must be converted to a discrete differential equation as given below Vo = Kp (e) + Ki e(t) dt + Kd (de(t)/dt) (1.1) At any instant of time, the current value of the PID output Vn is calculated based on the previous value of the PID output Vn-1, current error en, previous error en-1, previous to the previous error en-2, the cycle time T and weighing constant (Kp, Ki, Kd). The great advantage of the proposed control architecture is that the parameters of PID controllers do not need to adapt. Furthermore, the design scheme provides an easy way to design the PID controller. The goal of the first Ziegler-Nichols PID controller is designed with fast response. Usually, it can be obtained after using the Ziegler-Nichols tuning algorithms. The second gray prediction is that PID controller is operated in slow response. It can be easily achieved through scheduling the system output. Most of the control techniques implemented in industrial processes employ PID controller. There are two reasons why it is still 25

40 the majority in industrial processes. The first reason is that its simple structure and the well-known Ziegler-Nichols tuning algorithms have been developed [23]. The second reason is that the controlled processes in industrial plant can be controlled through the PID controller [24-25]. However, the conventional PID controller design usually needs to retune the parameters (proportional gain, integral time constant and derivative time constant) mutually by a skilled operator. In the present, easy but effective control architecture of PID controller integrating the well-known Ziegler-Nichols PID controller with gray prediction controller is introduced. In order to compensate the characteristic of original controller, the predicted system output feeds into the PID controller. Essentially, different system performance can be obtained if using the different prediction step. Proportional P Command + _ Error I Integral + + DAC & Driver Plant Output D Feedback ADC P Hardware Software Figure 1.8 Block diagram of software and hardware of PID control system 26

41 1.9 Ziegler-Nichols (ZN method) The PID tuning method is designed by Ziegler-Nichols (ZN) and is based on the systems step response. It uses the fact that many systems in the process industry can be approximated by a first-order lag plus a time delay of the step response of the planned [26]. In Ziegler and Nichols method based on the frequency response of the closed-loop system under pure proportional controlled, the gain is increased until the closed-loop system becomes critically stable. At this point the ultimate gain K is recorded together with the corresponding period of oscillation Tuknown as the ultimate period. Based on these values Ziegler-Nichols calculated the tuning parameters as shown in Table 1.1. Table 1.1 ZN PID frequency response tuning parameters S.no Controller K Ti Td 1 P 0.5 Ku 2 PI 0.4 Ku 0.8 Tu 3 PID 0.6 Ku 0.5 Tu 0.12 Tu The ZN methods were designed to give good responses to load disturbances. A quarter amplitude-damping criteria were used in the design giving a damping ratio close to 0.2. This is not satisfactory for many systems, since it does not give satisfactory phase and gain margins. The maximum sensitivity is also large, making systems sensitive to parameter variations. Additionally, ZN methods are not easy to apply in their original form on working plants. When critical processes are involved, sudden changes in the control signal or operation at the stability limit are not acceptable. Replay feedback and describing function analysis [27] are 27

42 often applied for parameter identification to overcome the above problems. A further modification to the ZN methods can give a substantially improved system performance [28]. The PID controller is very widely used in industry. Its popularity system from the fact that the control engineer essentially has to determine the best settings for the Proportional, Integral and Derivative action terms needed to achieve a desired closed-loop performance [29]. PID is a common feedback loop component in industrial control system. The controller takes a measured value from a process or other apparatus and compares it with a reference set point value. The difference (or error signal) is then used to adjust some input to the process in order to bring the process measured value to its desired set point. Unlike simpler controllers, the PID can adjust process outputs based on the history and rate of change of the error signal. This gives more accurate and stable control. In contrast to more complex algorithms such as optimal control theory, PID controllers can often be adjusted without advanced mathematics. However, pushing robustness and performance to the limits required a good understanding of the theory and controlled process [36-39]. PID control provides a generic and efficient solution to real-world controlled problems. The wide application of PID control has stimulated and sustained research and development to get the best out of PID, and The search is on to find the next key technology or methodology for PID tuning [1]. The proportional controller stabilizes the gain but produces a steady state error. The 28

43 integral controller reduces or eliminates the steady state error. The derivative controller reduces the rate of change of error. If the PID parameters (the gains of the proportional, integral and derivative terms) are chosen incorrectly, the controlled process input can be unstable, i.e. its output diverges, with or without oscillations, and is limited only by saturations or breakage. Tao and Sadler [30-35] designed a PID controller and applied non-linear programming techniques to determine the optimal controller gains presenting the best constant speed behavior for a four-bar mechanisms. A PID controller is called a PI, PD or P controller in the absence of respective control actions. The PID algorithm can be implemented in several ways. The easiest form to introduce is the parallel or non-interacting form, where the P, I and D elements are given the same error input in parallel. The output of the controller (i.e., the input to the process)is given by Output(t)=P contrib + I contrib + D contrib Where P contrib, I contrib, and D contrib are the feedback contributions from the PID controller, defined below: P contrib = Kpe(t) I contrib = 1/Ti e(t) dt D contrib = Td de/dt. Where e(t) = Set point Measurement (t) is the error signal and Kp, Ti, Td are constants that are used to tune the PID control loop: 29

44 1. Kp: Proportional Gain Larger K p typically means faster response since the larger the error, larger is the feedback to compensate. 2. Ti: Integral Time Small T i implies steady state errors are eliminated quicker. The tradeoff is larger overshoot: any negative error integrated during transient response must be integrated away by positive error before steady state is reached. 3. T d : Derivative Time Larger T d decreases overshoot, but slows down transient response [40-44]. 30

45 REFERENCES [1] Yun Li, Kiam Heong Ang, Chong, G.C.Y. Control Systems Magazine, IEEE Volume 26, Issue 1, Page(s): Digital Object Identifier /MCS , Feb [2] K J Astrom and T Hagglund, "Automatic Tuning of PID Controllers", The Control Handbook, Ed. W S Levine, IEEE Press, pp ,1996. [3] Katsuhiko Ogata, "Modern Control Engineering PHI", twelfth- Nov [4] Dr.A.K.Tandan, Dr.A.Subba Rao, Parag R. Desi, Dr. S.K.Kelkarni, "A course in Control Engineering", Dental rai & sons educational. sixth edition, [5] P. E. Silveira, R. de Souza Jr.,V. M. Biazotto, "Speed Control of an Autonomous Mobile Robot - Comparison between a PID Control and a Control Using Fuzzy Logic", J. Braz. Soc. Mech. Sci. vo1.24 no.2 Rio de Janeiro May [6] S Kelly, Vittal S Rao, Hardy. T Pottinger and II Clifford Bowman "Design and implementation of digital controllers for' smart structures using field programmable gate arrays", Smart Mater. Struct. 6,pp Printed in the UK [7] M K Refai, "Microprocessor-based digital controller for DC motor speed control", Butten~vorth & Co. (Publishers) Ltd, vol 10 no 10 December [8] Ogunnaike B.A, and Ray W. H, "Process Dynamics, Modeling, and Control", Oxford University Press, [9] Stephanopoulos, George, "Chemical Process Control - An Introduction to TheoryandPractice", Prentice-Hall of India Private Limited, [10] Smith C.A, and Corripio, A.B, Principles and Practice of Automatic Process Control:, John Wiley and Sons, [11] Kastauhiko Ogata, Modern control engineering, PIII, Twenty-third edition [12] I.J.Nagrath, M. Gopal. Control Systems Engineering New Age International. Third edition

46 [13] Joseph J. Distefano, Allen R, Stubberud, lvan J. Willieams, Theory and problems of feedback and control systems McGraw-Hill, [14] Gopal K. Dubey, Fundamentals of Electric Drives, Narosa Publishing House New Delhi, [15] B.G. Liptak, Instrument Engineers Handbook Process Control, Butterworth Heine Ltd, Oxford, [16] J. Michael Jacob, Industrial Control Electronics Applications and Design. Prentice Hall, Englewood Cliffs, [17] Kalsuhiko Ogata, Modern Control Engineering Prentice Hall of India Pvt. Ltd, [18] J. Nagrath and M. Gopal. Control Systems Engineering 3 rd ed, New Age International (P) Ltd, New Delhi [19] Bhaskar, P, Design and development of computer based instrumentation system for Photo acoustic studies. Ph.D. thesis, S.K. University, Anantapur, India, [20] NagoorKani, Control Systems 1 st ed, RBA publications, Chennai, [21] Richard C. Dorf and Robert H. Bishop, Modern Control systems, 8 th ed, Addison-Wiley, [22] William A, Wolovich, Automatic Control Systems : Basic Analysis and Design, Saunders College Publishing International ed, NY, [23] F. Franklin, J.D. Powell, E.N. Abbns, Feedback control of dynamic systems, Addison Wesley, [24] C. Kuo, Automatic control systems, 5 th edition, Engledwood Cliffs, N.J.: Prentice Hall [25] C.C. Hang, K.J,Astrom, W.K. Ho, Refinements of Ziegler-Nichols tuning formula, Proc. IEEE PT, D, 138, ,1991. [26] Astrom, K.J, and Hagglund, T, Automatic tuning of PID controllers, ISA, [27] Astrom. K.J, and Hagglund, T, PID control in The control handbook, pp , [28] Ziegler, J.G, and Nichols, N.B, Optimum settings for automatic controllers, Trans ASME-64, pp ,

47 [29] Neil Munro, The systematic design of PID controllers, The Institution of Electrical Engineers.9 the IEEE, Savoy Place, London WCZR OBL, UK, [30] Yi Zhangl, Chun Feng2, and Bailin Li2, PID Control of Nonlinear Motor- Mechanism Coupling System Using Artificial Neural Network, Chengudu, Sichuan , China, [31] Astrom K.J, T. Hagglund, C.C.Hang, and W.K. WO, Automatic tuning and adaptation for PID controllers and survey, Control Engineering Practice, Vol.1, pp , [32] Camcho, E.F. and C. Bordons, Model Predictive Control, Springer- Verlag London, [33] W.K. Ho, TH Lee, E.B. Tay, Knowledge-based multivariable PID control, Department of Electrical Engineering, National University of Singapore, Singapore, Received October [34] M H Moradi, New techniques of PID Controller Design, IEEE, pp. [ ], [35] P. Cominos and N. Munro PID controllers: recent tuning methods and design to Specification IEEE Proc.-Control theory appl. Vol. 149, No.1, January [36] Dong HwaKim and Jae Hoon Cha, Design of robust PID controller with disturbance rejection for motor using immune algorithm, in proc. IEEE, 4 th Int. Conf. on Hybrid Intelligent systems (HIS 04), [37] K. J. Astrom and T. Hagglong, PID Controllers: Theory, Design, and Tuning, Instrument Society of America, [38] Taifu Li. Jundi Xiong Rui Zhang Qlfu Tan. Ruizhend Xu, Hardware implementation of fuzzy PID controllers, Springer Science + Business Media, LLC [39] Curtis D. Johnson, Processes control instrumentation technology, Person education, Seventh edition, International Symposium on Neural Networks, vol. 3973, pp , [40] Yuon Fong Chan, Moallem, Wei Wang, Design and implementation of modular FPGA-Based PID Controllers, Industrial Electronics, IEEE Transactions on Volume 54, Issue 4, pp(s): , Aug

48 [41] M H Moradi, New techniques for PID Controller Design, IEEE, pp , [42] P. Cominos and N. Munro, PID controllers recent tuning methods and design to specification, HE Proc. Controller theory Appl. Vol. 149, NO.1, January [43] Ali Bekir Yildiz and M. Zeki Bilgin, Speed Control of Averaged DC Motor Drive Systemby Using Neuro-PiD Controller, Springer- VerlagBerlin Heidelberg [44] Brinkschulte, U, Pacher, M, Improving the real-time behavior of a multithreaded Java Microcontroller by control theory and model based latency prediction, 10 th IEEE International Workshop on Issue 2-4, pp.82-93, Feb

49 Chapter 2 REVIEW OF LITERATURE

50 The historical development of PID controller logic, the fundamentals of P, PI and PID theory and the basics of control strategies are introduced in the previous chapter for better understanding and design of the PID controllers. This chapter covers the comprehensive general and specific literature survey on ARM processors, methods and applications of P, PI, and PID logic controllers for process parameters in control system and solar trackers. Motivation for the present work is presented in the end. 2.1 General Literature Survey A Control system is an arrangement of physical components connected or related in such a manner as to command, direct or regulate itself or another system. The three-term functionality offering treatment of transient and steady state responses, PID control provides a generic and efficient solution to real-world control problems. The wide application of PID control has stimulated and sustained research and development technology or methodology for control applications. In recent years, a lot of research has been done in the area of automated vehicles and automatic highway systems. The embedded systems design challenges raise not only technology questions, but more importantly, requires the building of a new scientific foundation that systematically and even-handedly integrates, from the bottom up, computation and physically [1]. 35

51 In view of the elaborated literature survey in the field of speed control and to the best of knowledge, this research is focused on the application of PID logic controller for the process control as follows. Seugwan Ryu et al.,[2] proposed the proportional-integral (PI) proportional-derivative (PD) controller using proportional-integral-derivative (PID) feedback control to overcome the reactive control behavior of existing AQM proposals. They have given the comparative simulation study under a variety of network environments and PI-PD controller performance, random early detection, PI controller in terms of queue length dynamic, the packet loss rates, and the ink utilizations. The first speed-controlled drive was introduced over 100 years ago by Harry Ward Leonard in his paper Volts versus ohms speed regulation of electric motors, at a meeting of American Electrical Engineers. The rotating rectifier consisted of a grid-supplied induction machine that rotated a DC generator. By adjusting the magnetization of the DC generator, controllable DC voltage was available for the speed control of a DC motor. Although three machines were required, it was at the time the only possibility to realize a speed controlled drive. When the transistors and first micro-processors were introduced, chopper technologies such as the Pulse Width Modulated (PWM), enabled the accurate speed control of DC machines. Brushless DC motors with permanent magnets in the rotor were also introduced in the early 1960s, but since there were not powerful enough PM materials available yet, their power range was limited. However, with the availability of PMSM motors, and the advances in implementation of Field Oriented Control, PMSM dominated the motion control. The trend in the motion control nowadays is clearly 36

52 towards the brushless AC machines with sinusoidal excitation, which, in practice, means that a permanent magnet synchronous motor. Zhuo Ruan et al.,[3] reported a practically usable dual-mode PID control micro-system based on CPLD and reconfigurable FPGA. They have configured speed in the dual-mode control procedure, comparing with MCU and FPGA based real time system. The dualmode PID control is implemented on back proportional neural networks to convert all the controlling information into weight and threshold values and stored in BP neural network on the real time. They have mentioned that the controlled network or objective can be adjusted automatically and the labor burden could also be largely reduced. Once the external disturbing parameter largely amplified, the neural-network training can be restarted again so as to adjust controlling precision. 2.2 Literature Survey on P, PI and PID Control Jen-Yang Chen[4] reported a hybrid PID controller designed through fuzzy gain scheduling, instead of tuning the parameters of microcontroller used by conventional approaches. They mentioned that the great advantage of the approach is that parameters of original Ziegler Nicholas PID are unchanged through system operation. Basically, the approach provides an effective way to construct the PID controller. They studied the simulations of Hybrid PID controller and conventional Ziegler Nicholas PID controller. Jain Tang [5] presented real time DC motor control using the TMS320C31 DSP based system. A PID controller is designed using MATLAB functions to generate a set of coefficients associated with a desired controller's characteristics. The controller coefficients are then included in an assembly language program that implements 37

53 the PID controller. A digital PID controller was successfully implemented using the 31 DSK and tested on a DC motor speed and position control system for real time control of speed. The test results showed that with the PID controller added, the steady state error was eliminated and the desired output speed was obtained. M H Moradi et al.,[6] reported that optimal PID control signal similar to the Model-based Predictive Controller (MPC) signals. The controller reduces to the same structure of conventional PI and PID controller for first order and second order systems respectively. They have shown that the optimal values of PID gains are similar to a MPC. They mentioned that one of the main advantages of the proposed controller is that it can be used with systems of any order and the PID tuning can be used to adjust the controller performance. Yun Li et al.,[7] reported analysis and designing of PID control systems for steady state responses. They reported the problems involving the integral and derivative terms, and PID designing methods and future directions. They also discussed the wide application of PID control has stimulated and sustained research and development to get the best out of PID, and the search is on to find the next key technology or methodology for PID tuning. Basilio, J C et al., [8] reported the benchmark construction with application to PID controller design and implementation. A benchmark is constructed from a real system, a first order system with time delay, where the time delay and the time constant are both varying. The PI and PD controllers are designed for the benchmark plant and its implementation is done using the PID function. This value is processed by a programmable logic controller, generating an on/off type signal that drives the control circuit of a 38

54 solid-state relay, and causes the system to be fed (or not) by a sinusoidal voltage source of 220V. Finally, the real system simulation results are presented. P. Cominos and N. Munro [9] presented a tuning method of PID control with its advantages and disadvantages. They have concluded that the PID control is a promising control strategy that deserves further research and investigation. Recent research [10-12] into the design (tuning) of PID controllers has resulted in new ways of determining the values of the proportional Kp, integral Ki, and derivative Kd action settings for a given linear system-model. Yus of R.et al.,[13] reported the application of self-tuning PI controller to a water bath temperature control system. The complete setup was interfaced to the computer and control algorithms were developed using C. The results showed that the self-tuning PI performed better than PI controller. Manjunatha Reddy H. K [14] reported the implementation of proportional + integral + derivative controller (PIDC) in MATLAB environment for real time DC motor speed control. The obtained results show better performance of control action pertaining to rise time and steady state response. Liu et al.,[15] presented a robust tracking controller design for a short stroke permanent magnet motor as a linear drive. They designed PI and PID controllers. The simulation results of the PI and PID controllers designed using real actuator data. Even in the presence of high value exponential disturbance force, tracking of smooth trajectories ware carried out with errors not more than +60 microns. Leehter Yao and Chin-Chin Lin et al.,[16] presented proposed fuzzy PID controller that not only can be 39

55 considered as an adaptive fuzzy PID controller adapting with varying system dynamics, but also can be considered as a regular fuzzy PID controller with more flexibility. The proposed gain scheduled fuzzy PID controller performs well when controlling the system without varying dynamics. A. Visioli et al.,[17] presented fuzzy logic, for the tuning of proportional-integral-derivative (PID) controllers. A fuzzy inference system is adopted to determine the value of the weight that multiplies the set-point for the proportional action, based on the present output error and its time derivative. In this way, both the overshoot and the rise time in set-point following can be reduced. The values of the proportional gain and the integral and derivative time constant are found according to the well-known Ziegler-Nichols formula so that fine load disturbance attenuation is also concluded. Chandrasekhar T et al.,[18] presented embedded based DC motor speed control system is designed and developed. A PID controller was successfully implemented using the cygnal microcontroller and verified on a DC motor speed control system. For speed control, the test results showed that with the PID controller added, the desired output speed was obtained. Ibrahim Kaya et al.,[19] presented a PID controllers which are still extensively used in industrial systems. The PI-PD controller has been shown to provide very satisfactory closed loop performance for controlling processes with resonances, integrators and unstable transfer functions. And, they reported a simple approach to get parameters of a PI- PD controller from parameters of a PID controller so that a good closed loop system performance is obtained. Extensive simulation examples are given to 40

56 illustrate the value of the approach proposed. Jose L. Tong and James P. Bob is [20] presented a digital PID, which establishes a close relationship between the analog and the digital PID. The performances of designed/simulated digital PID controllers and analog PID are tested in the time domain and the frequency domain. Finally they mentioned that the digital PID is best representation from an analog PID controller. Nusret Tan et al.,[21] presented a simple method for the calculation of stabilizing PI controllers is given. The proposed method is based on plotting the stability boundary locus in the (kp, ki) plane and then computing stabilizing values of the parameters of a PI controller. The limiting values of the PID controller, which stabilize a given system are obtained in the (kp, kd)plane and (ki, kd)-plane. Jianxin Tang [22] presented real-time DC motor speed and position control using the TMS320C31 digital signal processing starter kit. A PID controller is designed using MATLAB functions to generate a set of coefficients associated with a desired controller's characteristics. Results show the improvement of system outputs as expected with a PID controller, with actual system outputs matching theoretical values. Mohamed I. Abu El-Sebah et al., [23] presented the application of a new PID controller with a PM motor drive system. The proposed controller algorithm has been deduced to be suitable for application to any process regardless of the process model or parameters. The drive system model has been developed and a simulation has been carried out for a performance prediction under different loading conditions with different command signals. The results of the simulation 41

57 indicate that the PID controller is effective and powerful for PM drive systems. A. Rubaai, M. J. Castro-Sitiriche et al., [24] presented an integrated environment for the rapid prototyping of a robust fuzzy-pid controller that allows rapid realization of novel designs. Both the design of the fuzzy PID controller and its integration with the classical PID in a global control system are developed. Experimental results show that the proposed hybrid fuzzy PID controller produces superior control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances. Dan Sun Jung Meng et al., [25] presented an adaptive single neuron based PID speed controller to substitute the traditional PID controller usually used in the fault tolerant 4-switch 3-phase (4S3Ph) inverter fed permanent magnet synchronous motor (PMSM) direct torque control (DTC) drive system to improve the system performance. Dong Hwa Kim et al., [26] presented design approach of PID controller with rejection function against external disturbance in motor control system using immune algorithm. Up to the present time, PID Controller has been used to operate for AC motor drive because of its implementation advantages in practice and simple structure. The parameters of PID controller are selected by immune algorithm to obtain the required response. Saeed Tavakoli et al., [27] presented tuning PID controllers for first order plus time delay systems by using dimensional analysis and numerical optimization techniques as an optimal method. And, reported that the proposed method has a considerable superiority over conventional techniques. In addition, the closed loop 42

58 system shows a robust performance in the face of model parameters uncertainty. Panagopoulos, H, Astrom, K.J et al.,[28] presented a new design method for PID controllers, based on optimization of load disturbance rejection with constraints on robustness to model uncertainties. The design also delivers parameters to deal with measurement noise and set-point response. Thus, the formulation of the design problem captures four essential aspects of industrial control problems, leading to a constrained optimization problem which can be solved iteratively. Bao-Gang Hu, Mann, G.K.I. Gosine, R.G. et al., [29] presented a function-based evaluation approach for a systematic study of fuzzy proportional-integralderivative (PID)-like controllers. This approach is applied for deriving processindependent design guidelines from addressing two issues: simplicity and nonlinearity. They reported the simplicity of fuzzy PID controllers and concluded that direct-action controllers exhibit simpler design properties than gainscheduling controllers. Gogoi, Manoj et al.,[30] presented a graphical design method for obtaining the entire range of PID controller gains that robustly stabilize a system in the presence of time delays and additive uncertainty is introduced. This design method primarily depends on the frequency response of the system, which can serve to reduce the complexities involved in plant modeling. The fact that time-delays and parametric uncertainties are almost always present in real time processes makes their controller design method very vital for process control. They have applied the design method to a DC motor model with a communication delay and a single area non-reheat steam generation unit. The results were satisfactory and robust 43

59 stability was achieved for the perturbed plants. Ho, K.W, Datta, A, and S.P. Bhattacharya et al.,[31] presented a set of PID controllers stabilizing a first-order plant with time-delay, and result to the case of an arbitrary order plant with time-delay. Sujoldzic, S. Watkins, J.M et al.,[32-33] presented a procedure for stabilizing a linear time-invariant plant of any order with time delay utilizing proportional-integral (PI) and proportional-derivative (PD) controllers. The method presented here is based on computing the stability boundary in terms of the proportional and integral gain for the PI case, and similarly, proportional and derivative gain for the PD case. Emami, T. and J.M. Watkins [34] presented a graphical technique for finding all proportional integral derivative (PID) controllers that stabilize a given single input-single-output (SISO) linear time-invariant (LTI) system of any order with time delay. They introduced a method that finds all PID controllers that also satisfy an H weighted sensitivity constraint. This problem can be solved by finding all PID controllers that simultaneously stabilize the closed-loop characteristic polynomial and satisfy constraints defined by a set of related complex polynomials. A key advantage of this procedure is the fact that it does not require the plant transfer function, only its frequency response. L. Mokrani and R. Abdessemed et al., [35] presented a novel design of a fuzzy-based selftuning TI controller (FSTPIC) for speed regulation of an indirect field-oriented induction motor (IM). In this new approach, the fuzzy tuning of a conventional PI controller gains is achieved through fuzzy rules deduced from many robustness simulation tests applied to several induction motors. These were applied for a 44

60 variety of operating conditions such as response to step speed command from standstill, step load torque application and speed reversion, with nominal parameters and an increased and/or decreased rotor resistance, self-inductance and inertia. Simulation results shown that the proposed fuzzy self-tuning PI controller is better than the fixed gains one in terms of robustness and speed rise time, even under great variations of operating conditions and load disturbance. Unji Yoshitsugu and Mutsuo Nakaoka et al., [36] presented an advanced method to improve the speed response characteristics of an AC servomotor drive system with mechanically resonant loads. Mechanical resonance and anti-resonance are both effectively suppressed on the basis of a load speed feedback approach for the AC servo motion system. Patricia Melin and Oscar Castillo [37] presented stepping motors control operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and a closed-loop control becomes necessary. And, fuzzy logic is applied to control the speed of a stepping motor drive with feedback. A neuro-fuzzy hybrid approach is used to design the fuzzy rule base of the intelligent system for control. Vijayakarthick, M. et al.,[38] presented Modified Repetitive Control Strategy (MRCS) and implemented in a DC motor. The MRCS incorporates the idea of repetitive control strategy (RCS) which accomplishes perfect asymptotic set point tracking in this process, provided that the period length used in the control formulation matches the actual period of the reference/disturbance signal exactly. The DC motor system is approximated into a 45

61 First Order plus Time Delay (FOPTD) model by step testing method. RCS is incorporated in the DC motor control loop of proportional (P) mode. The proportional controller parameter is obtained using Ziegler-Nichols Tuning Rule (ZNTR). The proposed MRCS is also integrated to a DC motor system. A periodic input signal of sine wave is generated and real time runs of the DC motor system are carried out for the periodic reference tracking with MRCS based P mode control loop. A similar run is also carried out with both RCS based P mode and conventional P-mode control structure in the loop. Huang, Y.Yasunobu, S., [39] reported a practical design method of fuzzy proportional-integral-derivative (PID) control system. Being simple structure, the research on how to choose the type of conventional PID controllers for different controlled plants is successful. Based on the analysis of relationship between conventional PID controller and fuzzy PID controller, they proposed a method on how to choose the type of fuzzy PID controller suitable for the controlled plant. Hagiwara, T., Yamada et al.,[40] proposed a design method for modified PID controllers such that the modified PID controller makes the control system for unstable plants stable and the admissible sets of P-parameter, I-parameter and D- parameter are independent from each other. When modified PID control systems are applied to real plants, the influence of disturbance in the plant is considered. In this study, they proposed a design method for modified PID control systems for multiple-input/multiple-output plants to attenuate unknown disturbances. El-Gammal. A.A.A. El-Samahy, A.A. et al.,[41] presented the application of a new particle swarm optimization technique for adjusting the gains of a PID speed 46

62 controller adaptively to give the minimum integral absolute error between the speed demand and the output response, minimum settling time, and minimum overshoot for a separately excited dc drive. The new technique converts all objective functions to a single objective function by deriving a single aggregate objective function using specified or selected weighing factors. Since the optimal PID controller parameters are dependent on the selected weighing factors, the weighing factors were also treated as dynamic optimizing parameters within the particle swarm optimization as a dual optimization and global selection of PID controller optimal parameters as well as best set of weighing factors. Yu Lin-ke, Zheng Jian-ming, et al.,[42] presented a fuzzy PID control algorithm by pulling in fuzzy control theory, which realizes self-tuning PID parameters and effectively weaken the system saturation, dead zone, the time delay and other disadvantages, the accuracy and stability of hydraulic position servo system are improved. The unit step, square wave and cosine input signals are simulated with PID control and fuzzy PID control. G. Sakthivel, T. S. Anandhi et al., [43] presented a fuzzy logic controller and conventional PI controller on an FPGA using VHDL for DC motor speed control. The proposed scheme is to improve tracking performance of D.C. motor as compared to the conventional (PI) control strategy. They described the hardware implementation of two inputs (error and change in error), one output fuzzy logic controller based on PI controller and conventional PI controller using VHDL. Real time implementation of FLC and conventional PI controller is made on Spartan-3A DSP FPGA) for the speed 47

63 control of DC motor. It is observed that fuzzy logic based controllers give better responses than the conventional PI controller for the speed control of dc motor. S. M. Giri Rajkumar et al.,[44] presented PID controller which has become inevitable in the process control industries due to its simplicity and effectiveness, but the real challenge lies in tuning them to meet the expectations. Although a host of methods already exist there is still a need for an advanced system for tuning these controllers. Computational intelligence (CI) has caught the eye of the researchers due to its simplicity, low computational cost and good performance, making it a possible choice for tuning of PID controllers, to increase their performance. This study describes in detail about Genetic Algorithm (GA), a CI technique, and its implementation in PID tuning for a real time industrial process which is closed loop in nature. Compared to other conventional PID tuning methods, the result shows that better performance can be achieved with the proposed method. Gopalakrishna G, Sivakumaran N, and Sivashanmugam P, et al., [45] proposed the temperature control of double tube heat exchanger system and presented an ant colony algorithm for optimizing PID parameters on the basis of conventional PID controller. The closed loop unit step response obtained with the proposed PID compares favorably with the one achieved using a conventional PID controller with dynamic closed-loop simulation. More important, the proposed approach takes a fraction of the time spent by the standard technique, without the need of perturbing the closed-loop system. Silva, G.J, Datta, A. et al., [46] proposed the problem of stabilizing a first-order plant with dead time using a proportional- 48

64 integral-derivative (PID) controller. Using a version of the Hermite-Biehler theorem that is applicable to quasi-polynomials, the complete set of stabilizing PID parameters is determined for both open-loop stable and unstable plants. The range of admissible proportional gains is first determined in closed form. For each proportional gain in this range, the stabilizing set in the space of the integral and derivative gains is shown to be a trapezoid, a triangle or a quadrilateral. For the case of an open-loop unstable plant, a necessary and sufficient condition on the time delay is determined for the existence of stabilizing PID controllers. C. C. Hang, K. J. Astrom et al.,[47] presented the accuracy of the Ziegler-Nichols tuning formula and reviewed in the context of PID auto tuning. For PID auto tuning, it is shown that, for excessive overshoot in the set-point response, set-point weighing can reduce the overshoot to specified values, and the original Ziegler- Nichols tuning formula can be retained. It is also shown that set-point weighing is superior to the conventional solution of reducing large overshoot by gain detuning or set-point filtering. However, for excessive set-point undershoots, the tuning formula will have to be modified. For PI auto tuning, it is shown that the Ziegler- Nichols tuning formula is inadequate and has to be completely revised. P. J. Gawthrop and D. E Nomikos et al.,[48] proposed the certain continuous-time self-tuning algorithms and shown to be capable of generating tuning parameters for commercial proportional-integral-derivative (PID) controllers. They are also shown to be capable of generating a feed forward signal that decouples the disturbance from the interaction from adjacent loops in a multivariable situation. M.B.B. Sharifian, R.Rahnavard et al., [49] presented at first a PID compensator 49

65 which adjusted by genetic algorithm then another compensator is designed by combining two methods, Integral controller and optimal State Feedback controller. In the second compensator, design specifications, depend on choosing weighing matrices using the Genetic Algorithm (GA) to find the proper weighing matrices. Of course Kalman filter is used as a system observer in order to increasing the system robustness. Then the performance of the control techniques is compared in terms of rise time, settling time, tracking error, and robustness with respect to modeling errors and disturbances. The controller design process and implementation requirements are also discussed. Then the comparison between the PID control and the optimal control shows that the optimal controller significantly reduced the overshoot, settling time and has the best performance encountering with system uncertainties. Asim Ali Khan, Nushkam Rapal et al.,[50] presented a fuzzy proportional integral derivative (PID) controller which can be tuned by carrying the tuning rules from PID domain to fuzzy domain. As a nonlinear controller, controlling a nonlinear process more efficiently, fuzzy controller can provide better performance in terms of rise time and smaller overshoot. The proposed controller is evaluated using some simulations. Mehdi Nasri et al., [51] presented a particle swarm optimization (PSO) method for determining the optimal proportional-integral derivative (PID) controller parameters, for speed control of a linear brushless DC motor. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The brushless DC motor is modeled in Simulink and the PSO algorithm is implemented in 50

66 MATLAB. Comparing with Genetic Algorithm (GA) and Linear quadratic regulator (LQR) method, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of a linear brushless DC motor. B. Nagaraj, S. Subha et al., [52] presented the parameters of PID controller tuned for controlling the armature controlled DC motor. Continuous cycling method & Z-N step response method are the conventional methods whose performance have been compared and analyzed with the intelligent tuning techniques like Genetic algorithm, Evolutionary programming and particle swarm optimization. GA, EP and PSO based tuning methods have proved their excellence in giving better results by improving the steady state characteristics and performance indices. Subrata Chattopadhyay et al., [53] presented a low cost operational amplifier based PID controller with inverse derivative control action has been described. Its transfer function has been derived and is found to be identical with the form already derived by other workers. It has been tested with a process plant analogue and implemented in the voltage control system of a DC generator. William L. Luyben, et al.,[54,55]presented about Genetic Algorithm (GA), a CI technique, and its implementation in PID tuning for a real time industrial process which is closed loop in nature. Compared to other conventional PID tuning methods, the result shows that better performance can be achieved with the proposed method. Ho W. K, Hang C. C, Cao L. S, et al., [56] presented simple formulae to tune/design the PI and PID controllers to meet user-specified gain 51

67 margin and phase margin. These formulae are particularly useful in the context of adaptive control and auto-tuning, where the controller parameters have to be calculated on-line. The results in this paper can be used to predict the achievable rise time of the closed-loop system, which is useful for self-diagnosis-a desirable feature of intelligent controllers. New insights into the internal model control design for the PID controller are also given[56].m. V. Sadasivarao and M. Chidambaram [57]presented a simple genetic algorithm and applied for tuning of PID controllers for the cascade control systems. A methodology for selecting the search region is proposed using Ziegler Nichols tuning method. Stability and robustness criteria are ensured in the selection of the search region, enabling the method to be applicable to online tuning. The inner and outer loops are tuned simultaneously, making the method applicable without disturbing the control strategy and ensuring overall optimal solution. The sum of integral absolute error values of the regulatory response is used as the objective function. Ashutosh K. Agarwal, Sanjeev KUMAR, et al., [58] proposed Genetic algorithms which are robust search techniques based on the principles of evolution. A genetic algorithm maintains a population of encoded solutions and guides the population towards the optimum solution. This important property of genetic algorithm is used to stabilize the inverted pendulum system. This paper highlights the application and stability of inverted pendulum using PID controller with fuzzy logic genetic algorithm supervisor. There are a large number of well-established search techniques in use within the information technology industry. They proposed a method to control inverted pendulum steady state error and overshoot 52

68 using genetic algorithm technique. Maruthai Suresh, et al., [59] presented a Controller tuning as the process of adjusting the parameters of the selected controller to achieve optimum response for the controlled process. For many of the control problems, a satisfactory performance is obtained by using PID controllers. One of the main problems with mathematical models of physical systems is that the parameters used in the models cannot be determined with absolute accuracy. The values of the parameters may change with time or various effects. In these cases, conventional controller tuning methods suffer when trying a lot to produce optimum response. In order to overcome these difficulties a fuzzy logic based Set- Point weighting controller tuning method is proposed. The effectiveness of the proposed scheme is analyzed through computer simulation using SIMULINK software. The fuzzy logic based simulation results are compared with Cohen-Coon (CC), Ziegler- Nichols (ZN), Ziegler Nichols with Set- Point weighting (ZN-SPW), Internal Model Control (IMC) and Internal model based PID controller responses (IMC-PID). The effects of process modeling errors and the importance of controller tuning have been brought out using the proposed control scheme. 2.3 Literature survey on DC motor speed control and applications DC motors are finding wider applications in the industry such as robotics, guided vehicles, chemical processes etc. Motor control for accurate positioning and speed is a very important function in many applications. Significant research has been done on the motor speed and position control using various controllers. 53

69 M.Hasheem Nehrir et al., [60] reported a microprocessor based DC motor speed control system and achieved in a manner understandable. The system integrates a microprocessor, a slow processing data acquisition system and a microcomputer to control the speed of the motor. M.S Khanniche et al., [61] reported a digital speed measurement scheme developed and implemented in real time using an Intel 16-bit microcontroller 80C196. It shows that % measurement accuracy is obtained based on a 360 pulses/rev shaft encoder. Results obtained from D.C drive which replaces digital speed loop indicates an improved speed control performance in comparison with conventional D.C drive. Samesh Asaad and Kevin Wareen et al., [62] proposed speed optimization of the circuit using an FPGA with embedded RAM. They mentioned the Active Line Repair (ALR) circuit as a specialized circuit to overcome some of the manufacturing imperfections in high resolution flat panel displays. They also discussed the designing of the ALR circuit and speed bottlenecks for an FPGAbased implementation and optimization alternatives. Results stress the importance of data representation and match to the underlying hardware resources such as embedded RAM blocks. Finally, the optimized circuit runs at 63 MHz system clock, achieving a 40% speedup over the design. Zhiliang Ding and Changde Wang et al., [63] presented the PID controller which is widely used in the automatic water conveyance system of canal. However, the conventional PID controller has poor adaptability to canal operating conditions, and along with the variation of canal operating conditions, one must continuously adjust the PID control parameters, it brings some difficulties to the practical operation. The 54

70 online self-adapting of PID parameters is also the problem difficult to solve of conventional PID controller, so the fuzzy control is combined with the PID control, designed the fuzzy self-adaptive PID controller, and applied it to the automatic control system of canal operation. When canal operates, according to actual response conditions, the computer use fuzzy reasoning, and the optimal adjustment of PID parameters can be automatically realized. Wang Hui, Yang Yongbo, Liu Meiyu et al., [64] represented an advanced Fuzzy - Intelligent PID controller which was applied in snow removal truck's hydraulic system by PID control and the intelligent combination of fuzzy control. The simulation experiments prove its feasibility. Zhiyun Zou, Dawei Han, Xinjun Gui et al., [65] reported small electric-heating reactors which are widely used in chemical experiments, fine and special chemical production processes. By summarizing the PID tuning experience and knowledge of human operators into fuzzy tuning rules, a fuzzy auto-tuning PID (FA-PID) control algorithm was developed for the temperature control of a small electric-heating reactor using the fuzzy tuning rules to tune PID parameters real-time on-line. Wei Zhng, Kun Wang, Shouzhi-L, [66] presented astringency and practicability of Particle Swarm Optimization Algorithm (PSO) and T cell's promotions and B cell's restrain ability of Immunity Particle Swarm Optimization Algorithm(IMPSO) and applied it to PID controllers. It is clear that IMPSO is suitable to increment PID control according to the simulations and it made the tracking and anti-jamming of IM PID based on IMPSO, IMPSO more effective than those of PID based on PSO and those of IMPID based on Immunity Algorithm. 55

71 Xiao-FengLi et al., [67] presented the control quality of conventional PID controller used in complex control system, a new design strategy of fuzzy-pid controller by utilizing the advantages of both fuzzy and auto tuning PID controls and their mutual compensation. The tuning method based on the specified phase and gain margin is proposed to determine the parameters of the new fuzzy selfadjusting PID controllers, to cope with a modern power plant with working condition changing frequently and strong dynamic time-varying nonlinear property. Using fuzzy inference methods, the fuzzy PID parameters can be adaptively adjusted on line for varying state of the system and changing operating condition. The strategy, which is implemented based on the function code on many typical DCS. The industrial application results show that many complex control systems that use this design strategy can effectively reduce the debugging time and has better control performance. YuzhenSun et al., [68] presented the biological immune system which has strong robustness and self-adaptability in circumstances full of disturbances and uncertainties, and the nonlinear PID has the advantages of simple algorithm, small overshoot, fast convergence. The fuzzy immune method and nonlinear PID are integrated together, and a novel nonlinear PID control strategy based on fuzzy immune algorithm is proposed. The strategy was simulated in the super heater steam temperature control system of power plants. And the immune genetic algorithm is used in tuning the control strategy's parameters. The simulation shows that the immune nonlinear PID has faster response speed, smaller overshoot, and shorter adjustment time and has better 56

72 dynamic performance. The controlling performance is better than that of the traditional PID control strategy. Li Chunwen et al., [69] presented the elevator kind of complex system with timevarying and strong-coupling characteristics. For elevator systems, with use of traditional PID algorithm, as there are disadvantages of difficult optimal parameters selection, weak steady-state behavior, etc., it is difficult to achieve satisfactory control effect. Therefore, they discussed the theory of using RBF neural network to identify control object, providing received Jacobian message to BP network, then using arbitrary nonlinear expression ability of BP neural network to achieve the optimum combination of PID control parameters through studying the system, and finally reaching the goal of speedy and stable control. Shanmugasundram et al., [70] presented an economical high-speed driver and converter circuits and a pulse width modulation (PWM) control strategy implemented in a versatile Aduc812 micro controller for achieving better performance. The results presented shows that this experimental set up will yield better performance. Kumar, C.A, Nair, N.K, [71] presented a Multi-Objective Optimization (EMOO) algorithm to tune the Proportional Integral speed regulator in the Permanent Magnet DC (PMDC) motor drive system and aims at achieving good robustness and struck at global optima. Calculus based methods including gradient approaches mainly search for local optimum solutions, rather than global optimum. Due to the drawbacks that exist in calculus based methods, the proposed 57

73 method is employed for multi objective optimization problems. The robustness features of the proposed design approach are verified using multi-objective evolutionary Non-dominated Sorting Genetic Algorithm (NSGA-II) and Non- Dominated Sorting Particle Swarm Optimization (NSPSO). Conflicting objectives considered are rise time and settling time for multi-objective optimization. Speed control of motor of Permanent Magnet DC Motor is achieved with PI speed regulator tuned using NSGA-II and NSPSO. Inaba, Hiromi, Onoda, Yoshimitsu, Shima, Seiya et al.,[71] presented a circuit with a unidirectional armature current and bidirectional field current was used in order to realize a high-reliability speed-control system for dc motors by simplifying the armature circuit construction in comparison with conventional Thyristor-Leonard speed-control systems. In making the new circuit feasible, a new system is developed in which either armature current or field current is fixed and the other varies depending on the magnitude of the torque command, a highreliability magnetically controlled three-phase thyristor amplifier, and minor feedback loops provided with a field current control circuit and armature current control circuit. By using these techniques, a dc motor control system is developed that features higher reliability and smaller power consumption than conventional control systems. The new system was applied to elevator control with good results. Dr. Mohammed Y. Hassan, Member, et al.,[73] reported the design of PID-like fuzzy logic controller (PIDFLC), on Field Programmable Gate Array (FPGA) 58

74 device. The Fuzzy Inference System (FIS) used in the controller is aided with Active Rules Selection Mechanism. Developments were made to this FIS to make it able to manipulate signed numbers (which is important issue in control system), then, it was blended with integral and derivative control components of tunable gains. These new features enable the controller to function as a PDFLC, a PIFLC, and a PIDFLC efficiently. B. Nagaraj, P. Vijayakumar et al., [74] proposed Soft computing technique and its implementation in PI tuning for a controller of a Pulp and paper industry process. Compared to other conventional PI tuning methods, the result shows that better performance can be achieved with the soft computing based tuning method. The ability of the designed controller in terms of tracking set point is also compared and simulation results are shown. 2.4 Literature Survey on Solar Tracking and Control Unit Solar energy is the radiant light and heat from the Sun has been exploited by humans since ancient times using a range of ever-evolving technologies. Solar radiation energy along with secondary solar resources such as wind and wave power, hydro-electricity and biomass account for most of the available renewable energy on earth. Only a little fraction of the available solar energy is used. Solar energy refers primarily to the use of solar radiation for practical ends. However, all renewable energies, other than tidal and geothermal, derive their energy from the sun. Solar technologies are broadly characterized as either passive or active depending on the way they capture, convert and distribute sunlight. Active solar techniques use photovoltaic panels, pumps, and fans to convert 59

75 sunlight into useful outputs. Passive solar techniques include selecting materials with favorable thermal properties, designing spaces that naturally circulate air, and referencing the position of a building to the Sun. Active solar technologies increase the supply of energy and are considered supply side technologies, while passive solar technologies reduce the need for alternate resources and are generally considered demand side technologies. Solar panels are devices that convert light into electricity. They are called solar after the sun or "Sol" because the sun is the most powerful source of the light available for use. They are sometimes called photovoltaic which means "lightelectricity". Solar cells or PV cells rely on the photovoltaic effect to absorb the energy of the sun and cause current to flow between two oppositely charge layers [75]. Fahrenbruch and Bube [76] presented a fundamental of solar cells theory and photovoltaic solar energy conversion. John Wiley & Sons [77] presented a solar cell and their applications A solar panel is a collection of solar cells. Although each solar cell provides a relatively small amount of power, many solar cells spread over a large area can provide enough power to be useful. To get the most power, solar panels have to be pointed directly at the Sun. The development of solar cell technology begins with 1839 research of French physicist Antoine-Cesar Becquerel. He observed the photovoltaic effect while experimenting with a solid electrode in an electrolyte solution. After that he saw a voltage developed when light fell upon the electrode.according to Encyclopedia Britannica the first genuine for solar panel 60

76 was built around 1883 by Charles Fritts [78]. He used junctions formed by coating selenium (a semiconductor) with an extremely thin layer of gold. Bull, S.R, et al.,[79] presented that energy is essential to our society to ensure our quality of life and to underpin all other elements of our economy. Renewable energy technologies offer the promise of clean, abundant energy gathered from self-renewing resources such as the sun, wind, earth, and plants. Virtually all regions of the United States and the world have renewable resources of one type or another. Renewable resources currently account for about 10% of the energy consumed in the United States, most of this is from hydropower and traditional biomass sources. Wind, solar, biomass, and geothermal technologies are costeffective today in an increasing number of markets, and are making important steps to broader commercialization. Each of the renewable energy technologies is in a different stage of research, development, and commercialization and all have differences in current and future expected costs, current industrial base, resource availability, and potential impact on greenhouse gas emissions. The technical status, cost, and applications of major renewable energy technologies and implications for increased adoption of renewable are reviewed. Rahman, S. et al.,[80] presented the interest in commercial green power in the developed world of about 25 years old, starting in the mid-1970s after the first oil shock. Electricity derived from any renewable energy source is considered "green" because of the negligible impact on greenhouse gas emissions. In terms of commercial energy, this list currently includes hydro, wind, biomass, geothermal and solar. In the 1970s and 1980s, the interest in green power was driven by the 61

77 goal of replacing fossil fuels to minimize the dependence on oil. Now there is a broader goal: to minimize the emission of CO 2 (the most common global warming gas) that results from the burning of fossil fuels. This article discusses the market potential for renewable resources, green power in the mainstream electric utilities, and the following renewable resources: hydroelectric power, wind power; biomass; solar thermal power; solar photovoltaic; and geothermal power. Beltran, J.A et al., [81] presented all the stages of development of a solar tracker for a photovoltaic panel. The system was made with a microcontroller which was designed as an embedded control. It has a data base of the angles of orientation horizontal axle, therefore it has no sensor inlet signal and it function as an open loop control system. Combined of the above mention characteristics in one, the tracker system is a new technique of the active type. It also has a rotational robot of 1 degree of freedom. M.A. Panait et al.,[82] presented among the non-conventional, renewable energy sources, solar energy affords great potential for conversion into electric power, able to ensure an important part of the electrical energy needs of the planet. The conversion principle of solar light into electricity, called Photo-Voltaic or PV conversion, is not very new, but the efficiency improvement of the PV conversion equipment is still one of the top priorities for many academic and/or industrial research groups all over the world. Among the proposed solutions for improving the efficiency of PV conversion, solar tracking is very significant. A. M. Morega et al.,[83] presented superior, scalable grid topology that may provide for higher quality of service, survivorship capacity, and reconfiguration capability as a 62

78 feature of modern grid architecture. The reported research is aimed at delivering a possible, constructive solution that is based on a minimum redundant, scalable, reconfigurable topology that helps increasing the grid immunity to faults. We assume that a tree network serves nodes of consumption that are evenly distributed throughout the territory. All nodes are equally important and network survivability means delivering electrical energy to as many consumers as possible. The models and loads estimation is based on the load momentum method. M. Morega et al.,[84] presented the minimization of the photovoltaic cells (PVC) electrical series resistance. They reported a theoretical step by step construction of optimal PVC, from the smallest, elemental cell to the largest assembly that relies on the minimization of the maximum voltage drop subject to volume (material) constraints. This completely deterministic approach produces optimal geometric shape for each assembly level, the optimal number and orientation of constituents within each new, higher order assembly and the optimal size of each new collector (metallic) path. P. I. Widenborg et al., [85] presented a new glass texturing method (AIT aluminum-induced texturisation) recently been developed by their group. The potential of this method is explored by fabricating plasma poly-si thin-film solar cells on glass superstrates that were textured with the AIT method. Using an inter digitated metallization scheme with a full-area Al rear contact, plasma cells with an efficiency of up to 7% are realized. This promising result shows that the AIT glass texturing method is fully compatible with the fabrication of poly-si thin-film 63

79 solar cells on glass using solid phase crystallization (SPC) of PECVD-deposited amorphous silicon precursor diodes. As such, there are now two distinctly different glass texturing methods-the AIT method and CSG Solar s glass bead method-that are known to be capable of producing efficient SPC poly-si thin-film solar cells on glass. Turmezei et al., [86] presented the problem of electrical energy storage that can possibly be solved with the help of electrochemical solar cells, which are suitable to generate either electrical energy or hydrogen gas under special conditions. The greatest problem of the electrochemical solar cell technology is to find novel materials which have appropriate properties for electrochemical energy conversion. J. Rizk et al.,[87] presented a potential system benefits of simple tracking solar system using a stepper motor and light sensor. This method has increasing power collection efficiency by developing a device that tracks the sun to keep the panel at a right angle to its rays. A solar tracking system is designed, implemented and experimentally tested. Kassem, A. et al.,[88] presented the renewable energy solutions that are becoming increasingly popular. Photovoltaic (solar) systems are but one example. Maximizing power output from a solar system is desirable to increase efficiency. In order to maximize power output from solar panels, one needs to keep the panels aligned with the sun. As such, a means of tracking the sun is required. This is definitely a more cost effective solution than purchasing additional solar panels. 64

80 Chung-Yuen et al.,[89] described a vector-controlled induction motor position servo motor drive where fuzzy control was used to achieve robustness against parameter variations and load torque disturbance effects. Paul-Hai Lin et al., [90] reported comparison on fuzzy logic and PID controls for a DC motor position controller. For comparison purpose, both fuzzy and PID control algorithms were implemented in 486-based PC with C program development tools. The experimental results showed that, PID control settling time given as minimum 198ms for a step size of 90and for the same the FLC has the 180ms. The conclusion drawn from the results was, the FLC has got better performance over PID. Haraldo Rodrigues De Azevedo et al., [91] proposed a fuzzy logic controller for DC motor position control. The results were presented in comparative terms among fuzzy logic controller, sliding mode controller and PID controller. In this application FLC proved to be better than the PID because PID is highly sensitive to the load changes while FLC almost insensitive. Jong-Hwan Kim et al., [92] reported fuzzy pre-compensated PID Controllers. They demonstrated the performance of their scheme via experiments performed on a DC servomotor position control tested under varying load conditions. They showed that the results of fuzzy pre-compensated PID controllers are superior to the conventional PID controller. Jung Sun Ko et al., [93] also presented a simple control for the robust position control of a brushless direct drive (BLDD) motor using FLC. The integral-proportional (IP) position controller plus fuzzy logic speed controller was employed to obtain the robust BLDD motor system, which was approximately linearized using the field orientation method for an AC servo. 65

81 Using the microprocessor, FLC controls the overall system and the robustness was also obtained without affecting the overall system response. Pai-Yi Huang et al., [94] designed fuzzy sliding mode controller (FSMC) based on real-coded genetic algorithm (RGA) for specified positioning. The real-coded genetic algorithm uses the internal floating-point representation of the computer system. The RGA based FSMC was applied to a high precision system. XY-able with high a highresolution laser scale was used as demonstration plant. The experimental results showed the position error and the desired voltage. R. Okuno et al., [95] developed DC motor position control system by using microprocessor. They employed PWM technique to control power to the system and the experimental results were presented for control of angle and control compliance. Shimada, A et al., [96] presented a position sensor less control technique on AC servo motor position control systems. The AC servo motor position sensor less control system applying the vector control method would be identical as in the case of the DC motor. Using vector control, the controller needs the data of the magnetic pole position on the rotor of the AC servo motor. By using the AC servo motor perfect position sensor less control technique, the controller should estimate both the magnetic pole position and mechanical position. Guang-hui et al., [97] presented an optimum proportional-integralderivative (PID) controller design for a PMDC motor position control. They attempt to combine the EP algorithm with the PID control design to solve the positioning control problem of a PMDC motor, such that a performance index of integrated-absolute error (IAE) is minimized. Last, a SYL-5 PMDC motor 66

82 position control system is used to verify the superiority of the proposed method. It can be easily seen from the simulation results that the proposed method has better performance than those presented in other studies. Phakamach, P et al., [98]presented a synchronous motor position control system using a discrete sliding mode model following control or DSMFC is presented to achieve accurate tracking in the presence of load disturbance and plant parameter variations. The DSMFC algorithm uses the combination of model following control and sliding mode control to improve the dynamic response for command tracking. A design procedure is developed for determining the control function, the coefficients of the switching plane and the integral control gain. The control function is derived to guarantee the existence of a sliding mode. A DSP-based synchronous motor position control system using the DSMFC approach is illustrated. Bousserhane et al., [99] presented the position control of linear induction motor using fuzzy sliding mode integral controller design. First, the indirect field oriented control LIM is derived. Then, a designed sliding mode integral control system with an integral-operation switching surface is investigated, in which a simple adaptive algorithm is utilized for generalized softswitching parameter. Finally, a fuzzy sliding mode controller is derived to compensate the uncertainties which occur in the control, in which the fuzzy logic system is used to dynamically control parameter settings of the SMIC control law. The effectiveness of the proposed control scheme is verified by numerical simulation. The numerical validation results of the proposed scheme have 67

83 presented good performances compared to the conventional sliding mode controller. Wang, W-J et al., [100] presented a passivity-based composite adaptive position control scheme for an induction motor. First, the dynamics of the induction motor is proved to be strictly passive and a composite adaptation algorithm is proposed to control the position of the induction motor. Then, the global stability of the induction motor position control system is formally proved by the passivity theory. Experimental results are provided to show that the good position tracking can be obtained without any information of the rotor flux. The proposed approach is robust to the variations of motor mechanical parameters and external load disturbances. Wen-Jieh Wang et al., [101] presented a passivity-based composite adaptive position control scheme for an induction motor. Stating the dynamics of the induction motor and a composite adaptation algorithm are to control the position of the induction motor. Bouzidi et al., [102] presented a comparison study between three DTC strategies applied in position control of an induction motor: (i) the Takahashi basic strategy using a two-level inverter and considering six sectors, (ii) a strategy based on the Takahashi one using a two-level inverter where twelve sectors have been considered instead of six and (iii) a strategy dedicated to high voltage applications using a three-level inverter and considering twelve sectors. Simulation results have clearly shown that the three DTC strategies offer a high dynamic of the motor position control. 68

84 Sicard et al., [103] presented the variable-structure system theory based robust DC motor position control system. The control law is numerically implemented for position control. The performance of the control law is illustrated by simulation results, taking into account sampling time, computation time delay, the current regulator, and limitations on the control signal. The proposed algorithm is easy to design and implement. Simulation results have shown its robustness toward parameter variations, good dynamic response was observed to a step in load torque, and a null static error. Chang, Y.-H et al., [104] presented a design and implementation of ah infin controller for a micro permanent- magnet synchronous motor position control system. The diameter of the micro motor is only 6 mm. In addition, the weighting functions of the H infin controller are selected using the genetic algorithms. A state-space H infin controller, obtained using a systematic design procedure, is incorporated in a closed-loop position control micro motor system. Jianying Liu et al., [105] presented a position control of a DC servo motor using PID Control algorithms. PID controllers are designed based on LabVIEW program, and the real-time position control of the DC servo motor was realized by using DAQ device. All codes are developed on The LabVIEW Real-Time Development System and then downloaded applications to run on PXI-8196 realtime controller of National Instruments. Min-Ho Park et al., [106] presented an induction motor position control system based on the sliding mode control. A lowpass filter is introduced between the sliding mode controller output and the motor controller input to reduce these effects. Although the filter smooth, motor input 69

85 current and alleviates the vibration at the final reference position, it may cause sluggish response in transient condition. To overcome the problem, a variablebandwidth filter is proposed. In steady state, the bandwidth of the filter is made to be narrow to mitigate the ripple components while it is widened during the transient to improve the response. Senjyu et al., [107] presented an ultrasonic motor that has nonlinear characteristics. To improve the control performance of the ultrasonic motor the dead-zone nonlinearity is eliminated. They proposed a new position control scheme for the ultrasonic motors that eliminates the problem due to dead-zone by employing fuzzy neural network (FNN). To achieve accurate position control when drive conditions vary, FNN shall adjust the membership function for antecedent part. Jung-Woo Cheon et al.,[108] presented a new control methodology to achieve accurate position control of an AC servo motor subjected to external disturbance. Unlike conventional sliding mode controller which requires a prior knowledge of the upper bound of external disturbance, the proposed technique, called sliding mode controller with disturbance estimator (SMCDE), can offer robust control performances without a prior knowledge of the disturbance bound. The SMCDE is featured by an integrated average value of the imposed disturbance over a certain sampling period. The benefits of the proposed control methodology are empirically demonstrated on AC servo motor and control responses are evaluated through a comparative work between the proposed and conventional control schemes. 70

86 Ankur Gupta et al., [109] presented a development of different control techniques such as Fuzzy, Sliding mode, and Sliding Mode fuzzy controllers for the servo drive system. Fuzzy controller gives better performance compared to other controllers but results are ineffective for disturbance case. Sliding mode fuzzy control is then implemented. It is observed that system performance increases when compared to fuzzy control for parameter variation case and for disturbance case which shows the robustness of SMFC. So sliding mode fuzzy control is superior when compared to other controller in terms of control performance. Various control schemes are then compared with each other. Simulations are carried out on MATLAB. Jhih-Da Hsu et al., [110] presented the design methodology to implement a fully digital control IC for the position control of the auto-focus (AF) lens module in applications to mobile phone camera using the field programmable gate array (FPGA). As compared with the conventional stepping motor control with a spring return fixture, the AF lens module is driven by a voice-coil motor (VCM) with a digital servo drive IC. The proposed digital servo drive IC includes a digital servo controller, a digital current controller, and a full-bridge dc-dc converter. The designed digital servo drive IC provides a total digital solution to the position control of the AF lens module in applications to high- performance light-weight slim-type mobile phone camera and video. Ku, C.L et al., [112] presented performance comparison of the position control of a linear permanent magnet brushless DC servo motor (LBLDCM) using linear proportional-integral derivative (PID) and non-linear sliding mode control (SMC) approach for pick and place application. As high precision positioning is involved 71

87 in the area of pick-and-place micro-assembly, a high performance controller is needed. Comparison of these two control schemes under different loads, acceleration, and pick-and-place time are presented. The control system is implemented on a general-purpose DSP DS1104 controller board. The servo motor used in this experiment is LMS20 C3, with maximum stroke length of 300 mm and with an encoder resolution of 0.1 m. Yi-Wei Tu et al., [113] presented the second-order controller structure that is chosen to encompass most of the classical control structures, such as PID controllers and lead/lag compensators. Based on results from parametric robust control and an extension of complex PID stabilization, a synthesis method for model matching of the interval plant is proposed. The obtained results are applied to the design of a controller for the speed control of a servomotor in the presence of load inertia variation and a sudden load change. Chang at el., [113] presented a recently the Time Delay Control (TDC) method has been proposed as a promising technique in the robust control area. This requirement imposes a severe limitation on the applications to most real systems. In order to solve this measurement problem, they proposed an observer design method that can stably reconstruct the state variables and their derivatives. Then, for a simulation study, the controller/observer based on their design method has been applied to a nonlinear plant, the result of which confirmed that the controller/observer performs satisfactorily as predicted. Daehie Hong et al., [114] presented the highly flexible and transparent actuator control, a fully-software-definable controller for various types of servo motors. 72

88 The flexible architecture of that controller was designed such that the control of the three types of motors, DC, brushless DC, and induction motors, can be freely configured and implemented on any hardware by a mere software modification. In order to achieve higher performance and reliability and also to make the system compact, the ASIC (application-specific integrated circuit) technology was fully utilized and one chip servo controller controlling logic was developed. Because of its flexibility and adaptability for distributed actuator control, the FSC is applied to the control of an electric powered wheeled mobile robot for highway maintenance operations. 2.5 Motivation for present work In the present research work, literature survey was carried out on solar tracking mechanism, ARM processor family, and application of PD, PI and PID logic controllers in the field of electromechanical and process control instrumentation. Most of the research was reported on the application of PID logic controller for controlling the process parameters. Yuen Fong Chan et. al.,[115]reported that the Modular designing of embedded feedback controllers using PID control. A novel distributed-arithmetic (DA) based proportional-integral-derivative (PID) controller algorithm is proposed and integrated into a digital feedback control system. By using the DA based PID controller, 80% savings in hardware utilization and 40% savings in power consumption are achieved compared to the multiple-based scheme. It also offers good closed-loop performance while using less resource, resulting in cost reduction, high speed, and low power consumption, which is desirable in embedded control applications. 73

89 Further, the designing of hardware is reduced the and real time implementation of PD, PI, PID logic controllers for process control is very rare. Even if so, it is not found in any research paper or report that the design of ARM Cortex microcontroller based complete hardware (including F/V converter, necessary signal conditioning, control circuitry, LCD and Keypad) and development of software to realize PID algorithms (using Embedded C language) for measurement and control of a process parameter. For most of the applications PC, microprocessor, and DSP based/matlab/simulations/plc/labview software and National Instruments hardware have been used. Hence it is taken as the motivation for the present work and it focuses on the design and development of both the hardware and software aspects of ARM Cortex microcontroller based PD, PI, and PID logic controllers for the process parameter such as DC motor speed measurement and controlling. The present study emphasizes the complete design of the microcontroller based control systems. 74

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101 Chapter 3 OVERVIEW OF EMBEDDED SYSTEMS

102 An embedded system is a dedicated system designed to perform a particular task within a larger system, often with real-time computing constraints[1 2]. It is embedded as part of a complete device often including hardware and mechanical parts. By contrast, a general-purpose computer, such as a personal computer (PC), is designed to be flexible and to meet a wide range of applications. Embedded systems control many devices in common[3]. Embedded systems contain either microcontrollers or digital signal processors (DSP) as processing cores[4]. The key characteristic, however, is being dedicated to handle a specific task. Since the embedded system is dedicated to particular tasks, the system is optimized to reduce the size and cost of the product and increase the reliability and performance. In various ways, embedded systems are reshaping the way people live, work, and play. An embedded system comes in an endless variety of types, each exhibiting unique characteristics. Embedded systems represent a class of dedicated computer systems designed for a particular task. In general embedded systems are reliable and predictable. The devices that embed them are convenient, user-friendly, and dependable. With drastic evolution embedded systems have changed the home environment and industrial environment. Perhaps the most significant application is robotic control parameters like speed and position controlling which is really just a very large collection of embedded systems that are interconnected using 86

103 various process and control technologies. Various industries use embedded systems for process control. The embedded systems for industrial use are designed to carry out specific tasks such as monitoring the temperature, pressure, humidity, voltage, current etc., and then take appropriate action. A general definition of embedded systems is that they are devices used to control, monitor or assist the operation of equipment, machinery or plant. Embedded reflects the fact that they are an integral part of the system. In many cases, their embeddedness may be such that their presence is far from obvious to the casual observer. Block diagram of a typical embedded system is shown in Figure3.1 Sensor Sensor Sensor Sensor Conditioning Processor And Peripherals Output Interfaces Actuator Indicator Figure 3.1 Typical embedded system 3.1 Characteristics Efficiency is of paramount importance for embedded systems. They are optimized for energy, code size, execution time, weight, dimensions, and cost. Embedded systems are typically designed to meet real time constraints; a real time system reacts to stimuli from the controlled object/ operator within the time interval dictated by the environment. For real time systems, right answers arriving too late (or even too early) are wrong. 87

104 Embedded systems often interact (sense, manipulate & communicate) with external world through sensors and actuators and hence are typically reactive systems, A reactive system is in continual interaction with the environment and executes at a pace determined by that environment. Embedded systems are application specific and single functioned, and the programs are executed repeatedly. They generally have minimal or no user interface. In an embedded system, the system software gets embedded with the application software, where as it is vice versa in personal computers. The very essence of an embedded system is iterative, step-wise execution of the embedded program with a stream of data [4]. Embedded system has only one build i.e., only one executable file. In this system the operating system is not a distinct entity. Only a required amount of system software exists to assist the application software. One of the most critical needs of an embedded system is to decrease power consumption, cost and space. This can be achieved by integrating more functions in to the CPU chip. Programming embedded systems is a special discipline and demands that embedded systems developers have working knowledge of a multitude of technology areas. These range from low-level hardware device, compiler technology, and debugging techniques, to the inner working of real time operating systems and multithreaded applications. Feature s embedded devices need to run multimedia applications demanding high computational power with low energy consumption constraints [5]. The embedded 88

105 world has been one of the more mature and relatively steady segments of the computing universe. Any embedded device can be made more flexible, useful, and often less expensive by designing it as an intelligent. Traditionally, embedded devices have been hardware-centric, and embedded software was relatively simple and carefully designed to optimize performance, and minimize the memory footprint. However, more than 70% of the development cost for complex system such as automotive electronics and communication systems is attributable to software development. This percentage is increasing constantly. Robotics is now becoming very powerful and carries interesting and complicated tasks such as hardware assembly. To facilitate the control of increasingly complex physical systems such as drive-by-wire automobiles and fly-by-wire aero-planes, embedded and networked computer systems with numerous hardware and software components are increasingly required [6]. Embedded systems in which some specific task has to be done in a specific time period are called real-time embedded systems. The development of embedded software was done mostly in assembly languages. However, due to the availability of cross compiler, most of the development is now in high-level languages such as C. Embedded systems are omnipresent and play significant roles in modern world. Traditional computer architecture/computer engineering curricula emphasize the hardware and software fundamentals suitable to general purpose computing. However, there is a growing realization that special purpose embedded systems computing requires a different educational emphasis than general purpose 89

106 computing. This microcomputer is an embedded chip, which is typically used for control applications, one emphasis must be on communications protocols with other devices such as RS-232, SPI, I 2 C, or CAN. Similarly, for interactions with the non-digital world, another emphasis must be on analog-to-digital and digitalto-analog conversion, because so many control applications are time-critical. Another emphasis must be on timing and interrupts represent only a small fraction of the total number of microprocessor applications. By some estimates, more than 90% of all microcontroller-based systems are special-purpose embedded systems rather than general-purpose computers. The microcontroller in embedded systems is typically optimized to perform a dedicated task, often a control application. An embedded system is found as a computing device that does a specific focused task. Applications such as the air conditioner, VCD player, DVD player, printer, fax machine, mobile phone etc., are examples of embedded systems. All these applications have a processor and special hardware to meet the specific requirement of the application along with the embedded software that is executed by the processor for meeting that specific requirement. The embedded software is also called firmware. The embedded system market is one of the highest growth area as these systems are used in the market segment of biomedical engineering, consumer electronics, data communication, office automation, industrial automation, wireless communications, telecommunications, transport, military and so on. Microcontroller contains memories, I/O lines, timers/counters, interrupts and serial communication. Using the microcontroller is greatest advantage, in its reduced hardware and increased efficiency. Typically, most embedded control 90

107 systems are designed around a microcontroller, which integrates on chip program memory and data memory and various peripherals such as timer/counters, serial communication, I/O ports. Microcontrollers are used in different applications, especially in real time applications. An embedded system product uses a microcontroller to do one and only one task, for example a printer is an embedded system since the microcontroller inside it performs one task only, namely getting the data and printing it. 3.2 Challenges and Recent trends in embedded systems Embedded software system is defined as a computing system that interacts with the physical world. This definition is incomplete, because every software system, once it is up and running interacts with the physical world. More precisely, what is meant is that an embedded software system has non-functional requirements, which concern the system's interaction with the physical world. Embedded systems constitute software programmable components interacting with dedicated hardware. Such systems are application specific systems containing software, hardware and communication channels tailored for a dedicated task. They are generally part of a larger system and are often candidates for (sub) system-on-achip realizations, SOCs, software provides features and flexibility and hardware provides performance. Apart from flexibility and performance, typical metrics include reliability, cost, size, weight, and power constraints. Many such applications in the IT-systems industry have continually changing specifications, and success depends strongly on time-to-market. This calls for a suitable and 91

108 improved process development model recognizing product life cycles and an efficient and integrated software/hardware development path Technology Challenges Embedded systems have become an integral part of the present world. Be it a music player, a cell phone, a smartcard, a router, or the electronics in an automobile - these systems have been touching and changing modern lives like never before. Embedded systems technology is the core in all intelligent products such as consumer electronic devices and automotive products. This is a yetevolving product class calling on technology from other classes, e.g. software, microprocessors, memory, analog and mixed signal systems, and reprogrammable circuits[7].the Embedded Systems Challenge at present years is about detecting hardware virus (Trojans) in a design. Hardware designer had inserted hardware trojans in some of the chips that they had manufactured. And challenge is to test each of the manufactured chips and classify which chips are infected with trojans and which chips are trojan-free. To achieve this purpose, one or more of the following trojan-detection techniques are used. 1. Functional test: Special functional test patterns are created to activate the trojan and observe its effect at the output. 2. Side-channel analysis: The delay, power consumption, or both parameters of the chip are analysed and the impact of Trojan is distinguished on these parameters from that of process variations. 92

109 Teams have to submit an initial report on their possible ideas on detecting trojans. Based on the report, finalists will be selected[8]. The first universal challenge in systems design is the construction of systems whose behavior can be predicted. In embedded systems, the interesting aspects of system behavior encompass not only functionality but also reaction and execution properties such as timing and resource consumption. For the sake of illustration, concentrate in the following on timing. For this purpose, use a notion of system behavior that includes, in addition to the values that are being computed, also the times at which the computed values become available. If other non-functional dimensions of behaviors are of interest, such as power consumption, then similar arguments can be made. Another universal challenge in embedded systems design is the construction of components whose behavior is robust in the presence of perturbations. While robustness is understood for most physical engineering artifacts, it is rarely heard of in connection with software. This is because computer programs can be readily idealized as discrete mathematical objects a perspective that has been advocated in computer science since the 1960s. If a program is studied as a discrete mathematical object, then correctness is a Boolean notion and can be established by proof: the program either satisfies its requirements or it does not. This prevalent view of programs as discrete partial functions on values or states has led to tremendous successes: it enabled the most fundamental paradigms of the 93

110 science of computing, including the theories of computability, complexity and semantics. In computer science, unlike in other engineering disciplines, often lose sight of the fact that the mathematical representation of a software system is just a model, and that the actual system is physical, executing on a physical imperfect platform and interacting with a physical unknowable environment. The realization that programs are ultimately physical shatters the Boolean illusion. Of two mathematically correct programs, one may be preferable to the other owing to the way it behaves if the platform or environment deviates from the nominal expectations, be it due to resource limitations, failures, attacks or simply erroneous or incomplete specifications. To some degree, this observation guides the design of robust non-embedded software, for example, by having the system check whether the input values lie within expected ranges. Moreover, one program may be more fault tolerant than another, functionally equivalent program, resilient against a larger class of potential attacks, etc. But the incompleteness of the sharp Boolean view becomes most apparent in embedded programming, where computing explicitly meets the physical world. This is because, often, the reaction and execution properties of a system such as response time or power consumption are best measured in terms of continuous quantities, and they may satisfy a design specification to different degrees[9]. 94

111 3.2.2 Recent Technology Trends The embedded systems industry was born with the invention of microcontrollers and since then it has evolved into various forms, from primarily being designed for machine control applications to various other new verticals with the convergence of communications. Over recent years, embedded systems have gained an enormous amount of processing power and functionality. Embedded computing is seeing a definite trend in migrating to 32-bit, 64-bit and from single to multi-core processors. Embedded systems meet their performance goals, including real-time constraints, through a combination of special-purpose hardware and software components tailored to the system requirements. Chip technology has undergone a very rapid improvement over several decades. The principal categories of improvement trends are shown in the table below: TREND Integration Level Cost Speed Power Compactness Functionality EXAMPLE Components/chip, Moore's Law Cost per function Microprocessor clock rate, GHz Laptop or cell phone battery life Small and lightweight products Non-volatile memory, Imager In particular, progress in semiconductor technology influences processor capabilities as evidenced by Computer architects are indebted to integrated circuit technology for these gains because it provides fuel for this revolution by shrinking densities and giving us more transistors. CMOS technology has followed Moore s law for 20 years. The real magic that computer architects have 95

112 managed is not only in using those faster transistors - giving a better clock rate - but also in making good use of the exploding number of transistors. Each linear shrink gives you a linear improvement in clock rate and a quadratic increase in the number of transistors [1]. These improvements have come about because of the industry's ability to exponentially shrink the geometrical feature sizes occurring in integrated circuits. The primary trend and a necessary condition for the other improvements, is the integration level expressed as Moore's Law, stating that the number of functionalities per chip and that CPU performance (MIPS) double every months. This is an observation/prediction made by Gordon Moore in 1965 prior to co-founding Intel, and it remains true up to 2002 and probably at least a decade after that as predicted by ITRS, (The International Technology Roadmap for Semiconductors). Embedded system is a complex object containing a significant percentage of electronic devices that interacts with the real world through sensing and actuating devices. The system is heterogeneous, as it is characterized by the coexistence of a large number of components such as microcontroller and Digital Signal Processing, as well as analog components such as A/D and D/A converters, sensors, transmitters and receivers. In the past, the system design effort has focused on these hardware parts, leaving the software design to be done afterwards as an implementation step. New software technologies are important for the future of control in an age of increasing complexity [10].For new 96

113 automotive applications and services, information technology has gained central importance. IT related costs in care manufacturing are already high and they will increase dramatically in the future [11]. The new embedded system is characterized by growing software complexity where embedded software dominates the development cost and schedule. Linux, for the first time in the industry, provides the potential of an open multi-vendor platform with an exploding base of software and hardware support. The growth in the use of Linux in embedded systems over the past few years has been astonishing. The success of Linux in the server or desktop arena over the last few years has received the most attention, where the most ardent supporters of Linux are attempting to loosen the strong hold of established operating systems such as Windows. In the embedded marketplace, in contrast, Linux is already moving toward world domination. In the embedded operating system UNIX, management of the graphic display is split between the X server, which knows the hardware and offers a unified interface to user programs[13]. A device driver plays a special role in the Linux kernel. They are distinct particular piece of hardware, which respond to a well-defined internal programming interface. User activities are performed by means of a set of standardized calls that are independent of the specific driver mapping. Those calls to device-specific operations that act on real hardware is then the role of the device driver. This programming interface is such that drivers can be built separately from the rest of the kernel, and plugged in at runtime when needed. 97

114 This modularity makes Linux drivers easy to write, to the point that there are now hundreds of them available. Recent trend in laboratory as well as in industrial automation designs uses minimal hardware and maximum support of software [14]. The rate at which new hardware becomes available alone guarantees that driver writers will be busy for the foreseeable future. Individuals may need to know about drivers in order to gain access to a particular device that is of interest to them. Hardware vendors, by making a Linux driver available for their products, can add the large and growing Linux user base to their potential markets. The pen source nature of the Linux system means that if the driver writer wishes, the source to a driver can be quickly disseminated to millions of users. But most of the principles and basic techniques are the same for all drivers. Multiprocessor systems, especially those based on multi core or multithreaded processors, and new operating system architectures can satisfy the ever increasing computational requirements of embedded systems [15]. To make device drivers, choosing an acceptable trade-off between the programming times required and the flexibility of the result is vital. A driver is flexible because it emphasizes that the role of a device driver is providing mechanism, not policy. The distinction between mechanism and policy is one of the best ideas behind the UNIX design. Most programming problems can indeed be split into two parts: what capabilities are to be provided (the mechanism) and how those capabilities can be used (the policy). If the two issues are addressed by different parts of the program, or even by different programs altogether, the software package is much easier to develop and to adapt to particular needs. The 98

115 driver should deal with making the hardware available, leaving all the issues about how to use the hardware to the applications. A driver, then is flexible if it offers access to the hardware capabilities without adding constraints. Model-Driven Development (MDD) is an emerging paradigm that uses Domain-Specific Modeling Languages (DSMLs) to provide correct-by-construction capabilities for many software development activities [16]. For example, a digital I/O driver may only offer byte-wide access to the hardware in order to avoid the extra code needed to handle individual bits. This privileged role of the driver allows the driver programmer to choose exactly how the device should appear. Different drivers can offer different capabilities, even for the same device. Many device drivers, indeed, are released together with user programs to help with configuration and access to the target device. Those programs can range from simple utilities to complete graphical applications. The ASICs (Applications Specific Integrating Circuits) are the third significant trend emerging in the embedded military market. A number of hardware/software co-design experiments to explore the design space and generate optimized implementations for specific application requirements [17]. With the advantages of small size, reliability, rapid response, compatibility to standard CMOS technology and on-chip signal processing, Ion-Sensitive Field effect Transistor (ISFET)-based transducers are increasingly being applied in physiological data acquisition and environment monitoring [18]. To reduce development cost and avoid duplication of design effort, FPGA prototypes and ASIC implementations are derived from a common source. Application-specific processors offer an 99

116 attractive option in the design of embedded systems by providing high performance for a specific application domain [19].The FPGAs and CPLDs are becoming a popular alternative to ASICs, providing integrators with an ideal alternative for satisfying feature/performance and environmental/cost requirements while meeting pressing time-to-market and time-to-deployment requirements. Unlike ASICs or custom circuits, the architecture of the FPGA is not fixed prior and therefore the permitted programmability, connectivity, and rout ability is constrained by that architecture [20]. Recent improvements in FPGA products, including significantly larger gate counts and software tools for development and integration, are now increasing their popularity. One of these significant improvements is the use of high-speed serial ports to connect FPGAs to a serial switching fabric, such as Rapid I/O. It uses Asymmetric SRAM (ASRAM) cell to implement the configuration memory [21].This trend provides a natural, high-speed, bi-directional data path that enables data movement at very high speeds. The FPGA is nonvolatile type and requires the battery back to stable the program, whereas CPLD is volatile type. The CPLD and FPGAs use VHDL or VeriLog language, as software. The Xillinx s Virtex-II, provirtex-v, Altera software are test bench tools that support VHDL and Verilog languages. When introducing VHDL, it is very important to keep emphasizing the fact that the VHDL code is only describing the required behavior of the digital circuit or system and is not being executed in some way by a hidden interpreter or microprocessor on the FPGA [22]. 100

117 To reduce development cost and avoid duplication of design effort, FPGA prototypes and ASIC implementations are derived from a common source. Application-specific processors offer an attractive option in the design of embedded systems by providing high performance for a specific application domain [19].This architecture is aimed at using hundreds of traditional reconfigurable field programmable gate arrays (FPGAs) to build the SOLAR (self-organizing learning array) learning machine. SOLAR has many advantages over the traditional neural network hardware implementation [23].The use of commercial SRAM-based FPGAs in satellites and spacecrafts present unique challenges in space radiation environment [24]. Silicon Laboratories manufacturers high performance industry s smallest mixed single chip microcontrollers. Technological advances have made it possible to integrate on a single chip enormous number of transistors, thus allowing the inclusion on a single chip of entire systems, including microprocessors, microcontroller, memories, ASICs, and peripherals. The development of this new class of systems is called Systems on Chip (SoCs) [25].The wizard generates both the framework code required to use the library and a project file that can be loaded into the Silicon Laboratories software development system. Flexible real-time kernel, called Yartek, is a low overhead and low footprint, suitable for embedded systems. Yartek has been developed on a Cold fire microcontroller. An embedded system developed for the implementation of non-visual perception for mobile robots [16].The system for temperature measuring was realized based on microcontroller and a digital temperature sensor [26]. 101

118 In the present study, the design, development, fabrication, and analysis of ARM Processor based PID logic controllers for DC motor speed and position control systems are discussed. The work element consists of microcontroller circuits, which by themselves act as specialized systems that internally contain some peripheral resources that facilitate their work and their relation with the outside world [27]. However, short-bit-width processors (8 bit processor) continue to dominate worldwide microcontroller sales volumes [28-30]. In 2006, the 8-bit units have continued to lead all microcontrollers in revenue and unit shipments [30]. Microcontrollers account for the majority of processors produced today, yet their capabilities are seldom explored in modern computer science curriculum. 3.3 ARM Processor Technology The simplicity of ARM processors makes them become dominant in the mobile and embedded electronics market, as low-cost, small microprocessors and microcontrollers. ARM processors account for 90% of all embedded processors and are used extensively in consumer electronics, including tablets, mobile phones, digital media and music players, hand-held game consoles, calculators and computer peripherals such as hard drives and routers. The reduced instructionset computing (RISC) microprocessor, peripherals, system-chip designs and compiler options favor to take advantage of ARM microprocessors. The ARM microprocessor range provides solutions for the open hardware platforms running complex operating systems for wireless, consumer, imaging applications, 102

119 embedded, real-time systems for mass storage, automotive, industrial. networking applications, smart cards and Single Inline Memory (SIM) modules. ARM is a 32-bit reduced instruction set computer (RISC) instruction set architecture (ISA). It was named the Advanced RISC Machine, and before that, the Acorn RISC Machine. Prominent ARM processor families developed include the ARM7, ARM9, ARM11 and Cortex. The official Acorn RISC Machine project started in October VLSI produced the first ARM silicon by April The first "real" production systems named ARM2 were available the following year. The original aim of a principally ARM-based computer was achieved in 1987 with the release of the Acorn Archimedes. The ARM2 featured a 32-bit data bus, a 26-bit address space and twenty-seven 32-bit registers. The ARM2 was the simplest useful 32-bit microprocessor in the world, with only 30,000 transistors. This simplicity led to its low power usage. A successor, ARM3, was produced with a 4 KB cache, which further improved the performance. From 1995 onwards, the ARM architecture reference manual has been the primary source of documentation on the ARM processor architecture and instruction set. The architecture has evolved over time, and starting with the Cortex series of cores, three "profiles" are defined: "Application" profile: Cortex- A series, "Real-time" profile: Cortex-R series, "Microcontroller" profile: Cortex- M series. To keep the design clean, simple and fast, the original ARM implementation was hardwired without microcode. The ARM architecture features includes Load/store architecture, Uniform bit register file, fixed 103

120 instruction width of 32 bits to ease decoding and pipelining, increased code density with thumb instruction set and mostly single-cycle execution. To compensate for the simpler design, compared with temporary processors additional design features were used: Conditional execution of most instructions, reducing branch overhead and compensating for the lack of a predictor. Arithmetic instructions alter condition codes only when desired, like 32-bit barrel shifter which can be used without performance penalty with most arithmetic instructions and address calculations, powerful indexed addressing modes, link register for fast leaf function calls, and simple, but fast, 2-priority-level interrupt subsystem with switched register banks. There are special mechanisms for addressing coprocessors in the ARM architecture. In ARM-based machines, peripheral devices are usually attached to the processor by mapping their physical registers into ARM memory space or into the coprocessor space or connecting to another device (a bus) which in turn attaches to the processor. All modern ARM processors include hardware debugging facilities using JTAG support, without them, software debuggers could not perform basic operations like halting, stepping, and break pointing of code starting from reset. The ARMv7 architecture defines basic debug facilities at an architectural level. These include breakpoints, watch points, and instruction execution in a "Debug Mode", similar facilities were also available with embedded ICE[31]. To improve the ARM architecture for digital signal processing and multimedia applications, a few new instructions were added to the set. VFP (Vector Floating 104

121 Point) technology is an FPU coprocessor extension to the ARM architecture that provides low-cost single-precision and double-precision floating-point computation fully compliant with the ANSI/IEEE Std Standard for Binary Floating-Point Arithmetic[32]. VFP provides floating-point computation suitable for a wide spectrum of applications. NEON extension features a comprehensive instruction set, separate register files and independent execution hardware. NEON supports 8-, 16-, 32- and 64-bit integer and single-precision (32- bit) floating-point data. Analog Devices, Intel, IBM, and Texas Instruments are some of the many companies who have licensed the ARM in one form or another. The ARM architecture is supported by a large number of embedded and real-time operating systems. The ARM architecture is supported by Unix and Unix-like operating systems and Microsoft demonstrated a preliminary version of Windows running on an ARM-based computer [33] Cortex-M3 Processors The ARM Cortex -M3 processor is the industry-leading 32-bit processor for highly deterministic real-time applications and has been specifically to develop high-performance low-cost platforms for a broad range of devices including microcontrollers, industrial control systems and wireless networking and sensors[34]. The processor delivers outstanding computational performance and exceptional system response to events while meeting the challenges of low dynamic and static power constraints. The processor is highly configurable enabling a wide range of implementations from those requiring memory 105

122 protection and powerful trace technology to extremely cost sensitive devices requiring minimal area. Delivering higher performance and richer features The Cortex-M3 is the mainstream ARM processor developed specifically with microcontroller applications in mind. Block diagram of Cortex-M3 processor is shown in Figure 3.2. WIC NMC ARM ARM Core ETM DAP Memory Processing Unit Serial Wire Viewer Cortex M3 Data Watch Point Flash Patch Bus Matrix CodeInte rface SRAM & Peripheral I/F Fig. 3.2 Block diagram of Cortex-M3 processor Performance and Energy Efficiency With high performance and low dynamic power consumption the Cortex-M3 processor delivers leading power efficiency 12.5 DMIPS/mw based on 90nmG. Coupled with integrated sleep modes and optional state retention capabilities the Cortex-M3 processor ensures there is no compromise for applications requiring low power and excellent performance. 106

123 Full featured The processor executes Thumb -2 instruction set for optimal performance and code size, including hardware division, single cycle multiply, and bit-field manipulation. The Cortex-M3 NVIC is highly configurable at design time to deliver up to 240 system interrupts with individual priorities, dynamic reprioritization and integrated system clock. Rich connectivity The combination of features and performance enables Cortex-M3 based devices to efficiently handle with multiple I/O channels and protocol standards such as USB OTG Target Applications: The Stellaris family is positioned for cost-conscious applications requiring significant control processing and connectivity capabilities such as: Gaming equipment Network appliances and switches Home and commercial site monitoring and control Electronic point-of-sale (POS) machines Motion control Medical instrumentation Remote connectivity and monitoring Test and measurement equipment Factory automation Fire and security Lighting control Transportation 107

124 3.3.3 Architectural Overview 32-bit capabilities and the full benefits of ARM Cortex -M-based microcontrollers later to the broadest reach of the microcontroller market. For current users of 8- and 16-bit MCUs, Stellaris with Cortex-M offers a direct path to the strongest ecosystem of development tools, software and knowledge in the industry. Designers who migrate to Stellaris benefit from great tools, small code footprint and outstanding performance. For users of current 32-bit MCUs, the Stellaris family offers the industry s first implementation of Cortex-M3 and the Thumb-2 instruction set. With blazingly-fast responsiveness, Thumb-2 technology combines both 16-bit and 32-bit instructions to deliver the best balance of code density and performance. Thumb-2 uses 26 percent less memory than pure 32-bit code to reduce system cost while delivering 25 percent better performance. The Texas Instruments Stellaris family of microcontrollers the first ARM Cortex-M3 based controllers brings high-performance 32-bit computing to cost-sensitive embedded microcontroller applications. The LM3S9B96 microcontroller combines complex integration and high performance with the following feature highlights: ARM Cortex-M3 Processor Core High Performance: 80-MHz operation, 100 DMIPS performance 256 KB single-cycle Flash memory 96 KB single-cycle SRAM External Peripheral Interface (EPI) 108

125 Advanced Communication Interfaces: UART, SSI, I2C, I2S, CAN, Ethernet MAC and PHY, USB. System Integration: general-purpose timers, watchdog timers, DMA, general-purpose I/Os. Advanced motion control using PWMs, fault inputs, and quadrature encoder inputs Analog support: analog and digital comparators, Analog-to-Digital Converters (ADC), on-chip voltage regulator JTAG and ARM Serial Wire Debug (SWD) 100-pin LQFP package. 108-ball BGA package. Industrial (-40 C to 85 C) temperature range. Figure 3.3 depicts the architectural features of the LM3S9B96 microcontroller. There are two on-chip buses that connect the core to the peripherals. The Advanced Peripheral Bus(APB) is the legacy bus. The Advanced High- Performance Bus (AHB) bus provides better back-to-back access performance than the APB bus. 109

126 JTAG/SWD ARM Cortex M3 (80 Mhz) DCode Bus ROM Boot Loader DriverLib AES & CRC SafeRTOS System Control and clock ICode Bus Flash (256KB ) BUS Matrix SRAM DMA SYSTEM PERIPHERALS Watch Kp GPIOs General Purpose ( ) External Peripheral SERIAL PERIPHERALS USB OTG (FS PHY) SSI UART Process I2C CAN Controller Ethernet Feedback I2S ANALOG Analog Comparator 10 Bit ADC Channels MOTION CONTROL PWM QEI Figure 3.3 LM3S9B96 microcontrollers architecture 110

127 In addition, the LM3S9B96 microcontroller offers the advantages of ARM's widely available development tools, System-on-Chip (SoC) infrastructure IP applications, and a large user community. Additionally, the microcontroller uses ARM's Thumb -compatible Thumb-2 instruction set to reduce memory requirements and, thereby, cost. Finally, the LM3S9B96 microcontroller is codecompatible to all members of the extensive Stellaris family, providing flexibility to fit precise needs. Texas Instruments offers a complete solution to get to market quickly, with evaluation and development boards, white papers and application notes, an easy-to-use peripheral driver library, and a strong support, sales, and distributor network Pin Diagram: The LM3S9B96 microcontroller pin diagram is shown in Figure 3.4.Each GPIO signal is identified by its GPIO port unless it defaults to an alternate function on reset. In this case, the GPIO port name is followed by the default alternate function. Figure 3.4: Pin diagram of LM3b9B96 microcontroller 111

128 3.3.5 Features The LM3S9B96 microcontroller component features and general function are discussed in more detail in the following section. All members of the Stellaris product family, including the LM3S9B96 microcontroller, are designed around an ARM Cortex-M3 processor core. The ARM Cortex-M3 processor provides the core for a high-performance; low-cost platform that meets the needs of minimal memory implementation, reduced pin count, and low power consumption, while delivering outstanding computational performance and exceptional system response to interrupts. 32-bit ARM Cortex-M3 architecture optimized for small-footprint embedded applications 80-MHz operation, 100 DMIPS performance. Outstanding processing performance combined with fast interrupt handling Thumb-2 mixed 16-/32-bit instruction set delivers the high performance expected of a 32-bitARM core in a compact memory size usually associated with 8- and 16-bit devices, typically in the range of a few kilobytes of memory for microcontroller-class applications. Single-cycle multiply instruction and hardware divide. Automatic bit manipulation (bit-banding), delivering maximum memory utilization and streamlined peripheral control. Unaligned data access, enabling data to be efficiently packed into memory. 112

129 Fast code execution permits slower processor clock or increases sleep mode time. Harvard architecture characterized by separate buses for instruction and data. Efficient processor core, system and memories. Hardware division and fast digital-signal-processing. Saturating arithmetic for signal processing. Deterministic, high-performance interrupt handling for time-critical applications. Memory protection unit (MPU) to provide a privileged mode for protected operating system functionality. Enhanced system debug with extensive breakpoint and trace capabilities Serial Wire Debug and Serial Wire Trace reduce the number of pins required for debugging and tracing. Migration from the ARM7 processor family for better performance and power efficiency. Optimized for single-cycle Flash memory usage. Ultra-low power consumption with integrated sleep modes System Timer (Sys Tick) ARM Cortex-M3 includes an integrated system timer, SysTick. SysTick provides a simple, 24-bit, clear-on-write, decrementing, wrap-on-zero counter with a 113

130 flexible control mechanism. The counter can be used in several different ways, for example: An RTOS tick timer that fires at a programmable rate (for example, 100 Hz) and invokes a SysTick routine A high-speed alarm timer using the system clock A variable rate alarm or signal timer the duration is range-dependent on the reference clock used and the dynamic range of the counter A simple counter used to measure time to completion and time used An internal clock-source control based on missing/meeting durations Interrupt Controller: The LM3S9B96 controller includes the ARM Nested Vectored Interrupt Controller (NVIC). The NVIC and Cortex-M3 prioritizes and handles all exceptions in Handler Mode. The processor state is automatically stored to the stack on an exception and automatically restored from the stack at the end of the Interrupt Service Routine (ISR). The interrupt vector is fetched in parallel to the state saving, enabling efficient interrupt entry. The processor supports tailchaining, meaning that back-to-back interrupts can be performed without the overhead of state saving and restoration. Software can set eight priority levels on 7 exceptions (system handlers) and 53 interrupts. Deterministic, fast interrupt processing: always 12 cycles, or just 6 cycles with tail-chaining. 114

131 External non-maskable interrupt signal (NMI) available for immediate execution of NMI handler for safety critical applications. Dynamically reprioritizable interrupts. Exceptional interrupt handling via hardware implementation of required register manipulations ON-Chip Memory: The LM3S9B96 microcontroller is integrated with the following set of on-chip memory and features: 96 KB single-cycle SRAM 256 KB single-cycle Flash memory up to 50 MHz, a pre-fetch buffer improves performance above50 MHz Internal ROM loaded with software: Peripheral Driver Library Boot Loader Safe RTOS kernel Advanced Encryption Standard (AES) cryptography tables Cyclic Redundancy Check (CRC) error detection functionality SRAM : The LM3S9B96 microcontroller provides 96 KB of single-cycle on-chip SRAM. The internal SRAM of the Stellaris devices are located at offset 0x of the device memory map. Because read-modify-write (RMW) operations are very time consuming, ARM has introduced Bit-banding technology in the Cortex-M3 115

132 processor. With a bit-band-enabled processor, certain regions in the memory map (SRAM and peripheral space) can use address aliases to access individual bits in a single, automatic operation. Data can be transferred to and from the SRAM using the Micro Direct Memory Access Controller (μdma) Flash Memory The LM3S9B96 microcontroller provides 256 KB of single-cycle on-chip Flash memory. The Flash memory is organized as a set of 1-KB blocks that can be individually erased. Erasing a block causes the entire contents of the block to be reset to all 1s.These blocks are paired into a set of 2-KB blocks that can be individually protected. The blocks can be marked as read-only or execute-only, providing different levels of code protection. Read-only blocks cannot be erased or programmed, protecting the contents of those blocks from being modified. Execute-only blocks cannot be erased or programmed, and can only be read by the controller instruction fetch mechanism, protecting the contents of those blocks from being read by either the controller or by a debugger ROM The LM3S9B96 ROM is preprogrammed with the following software and programs: Peripheral Driver Library Boot Loader Safe RTOS preemptive real-time kernel Advanced Encryption Standard (AES) cryptography tables 116

133 Cyclic Redundancy Check (CRC) error-detection functionality External Peripheral Interface: The External Peripheral Interface (EPI) provides access to external devices using a parallel path. Unlike communications peripherals such as SSI, UART, and I C, the EPI is designed to act like abus to external peripherals and memory. The EPI has the following features: 8/16/32-bit dedicated parallel bus for external peripherals and memory Memory interface supports continuous memory access independent of data bus width, thus enabling code execution directly from SDRAM, SRAM and Flash memory. Blocking and non-blocking reads. Separates processor from timing details through use of an internal write FIFO. Efficient transfers using Micro Direct Memory Access Controller (μdma). Separate channels for read and write Read channel request asserted by programmable levels on the internal non-blocking read FIFO (NBRFIFO) Write channel request asserted by empty on the internal write FIFO (WFIFO) 117

134 The EPI supports three primary functional modes: Synchronous Dynamic Random Access Memory (SDRAM) mode, Traditional Host-Bus mode, and General- Purpose mode. The EPI module also provides custom GPIOs, however, unlike regular GPIOs, the EPI module uses a FIFO in the same way as a communication mechanism and is speed-controlled using clocking. Synchronous Dynamic Random Access Memory (SDRAM) mode Supports SDRAM at up to 50 MHz Supports low-cost SDRAMs up to 64 MB (512 megabits) Includes automatic refresh and access to all banks/rows Includes a sleep/standby mode to keep contents active with minimal power draw Multiplexed address/data interface for reduced pin count Host-Bus mode Traditional x8 and x16 MCU bus interface capabilities. Similar device compatibility options as PIC, AT mega, 8051, and others. Access to SRAM, NOR Flash memory, and other devices, with up to 1 MB of addressing in multiplexed mode and 256 MB in multiplexed mode (512 MB in Host-Bus 16 mode with no byte selects). Support of both muxed and de-muxed address and data. 118

135 Access to a range of devices supporting the non-address FIFO x8 and x16 interface variant, with support for external FIFO (XFIFO) EMPTY and FULL signals. Speed controlled, with read and writes data wait-state counters. Chip select modes include ALE, CSn, Dual CSn and ALE with dual CSn. Manual chip-enable (or use extra address pins). General-Purpose mode Wide parallel interfaces for fast communications with CPLDs and FPGAs Data widths up to 32 bits. Data rates up to 150 MB/second. Optional "address" sizes from 4 bits to 20 bits. Optional clock output, read/write strobes, framing (with counterbased size), and clock-enable input. General parallel GPIO 1 to 32 bits, FIFO with speed control. Useful for custom peripherals or for digital data acquisition and actuator controls Serial Communications Peripherals: The LM3S9B96 controller supports both asynchronous and synchronous serial communications with: 119

136 10/100 Ethernet MAC and PHY with IEEE 1588 PTP hardware support. Two CAN 2.0 A/B controllers. USB 2.0 OTG/Host/Device. Three UARTs with IrDA and ISO 7816 support (one UART with modem flow control and status) Two I C modules. Two Synchronous Serial Interface modules (SSI). Integrated Inter-chip Sound (I2S) module. The following sections provide more detail on each of these communications functions Ethernet Controller: Ethernet is a frame-based computer networking technology for local area networks (LANs). Ethernet has been standardized as IEEE This specification defines a number of wiring and signaling standards for the physical layer, two means of network access at the Media Access Control (MAC)/Data Link Layer, and a common addressing format. The Stellaris Ethernet Controller consists of a fully integrated media access controller (MAC) and network physical (PHY) interface and has the following features: Conforms to the IEEE specification 10BASE-T/100BASE-TX IEEE compliant. Requires only a dual 1:1 isolation transformer interface to the line 120

137 10BASE-T/100BASE-TX ENDEC, 100BASE-TX scrambler/ descrambler Full-featured auto-negotiation Multiple operational modes Full- and half-duplex 100 Mbps Full- and half-duplex 10 Mbps Power-saving and power-down modes Highly configurable Programmable MAC address LED activity selection Promiscuous mode support CRC error-rejection control User-configurable interrupts Physical media manipulation MDI/MDI-X cross-over support through software assist Register-programmable transmits amplitude Automatic polarity correction and 10BASE-T signal reception IEEE 1588 Precision Time Protocol: Provides highly accurate time stamps for individual packets. Efficient transfers using Micro Direct Memory Access Controller (μdma). 121

138 Separate channels for transmit and receive Receive channel request asserted on packet receipt Transmit channel request asserted on empty transmit FIFO As shown in Figure 3.5, the Ethernet Controller is functionally divided into two layers: The Media Access Controller (MAC) layer and the Network Physical (PHY) layer. These layers correspond to the OSI model layers 2 and 1, respectively. The CPU accesses the Ethernet Controller via the MAC layer. The MAC layer provides transmits and receives processing for Ethernet frames. The MAC layer also provides the interface to the PHY layer via an internal Media Independent Interface (MII). The PHY layer communicates with the Ethernet bus [18]. Figure 3.5 Ethernet Controller Pulse Width Modulation (PWM): Pulse width modulation (PWM) is a powerful technique for digitally encoding analog signal levels. High-resolution counters are used to generate a square wave, and the duty cycle of the square wave is modulated to encode an analog signal. Typical applications include switching power supplies and motor control. The 122

139 LM3S9B96 PWM module consists of four PWM generator blocks and a control block. Each PWM generator block contains one timer (16-bit down or up/down counter), two comparators, a PWM signal generator, a dead-band generator, and an interrupt/adc-trigger selector. Each PWM generator block produces two PWM signals that can either be independent signals or a single pair of complementary signals with dead-band delays inserted. Each PWM generator has the following features: Four fault-condition handling inputs to quickly provide low-latency shutdown and prevent damage to the motor being controlled. One 16-bit counter. Runs in down or Up/Down mode. Output frequency controlled by a 16-bit load value. Load value updates can be synchronized. Produces output signals at zero and load value. Two PWM comparators Comparator value updates can be synchronized Produces output signals on match PWM signal generator. Output PWM signal is constructed based on actions taken as a result of the counter and PWM comparator output signals Produces two independent PWM signals. 123

140 Dead-band generator Produces two PWM signals with programmable dead-band delays suitable for driving a half-h bridge Can be bypassed, leaving input PWM signals unmodified. Can initiate an ADC sample sequence: The control block determines the polarity of the PWM signals and which signals are passed through to the pins. The output of the PWM generation blocks are managed by the output control block before being passed to the device pins. The PWM control block has the following options: PWM output enable of each PWM signal. Optional output inversion of each PWM signal (polarity control). Optional fault handling for each PWM signal. Synchronization of timers in the PWM generator blocks. Synchronization of timer/comparator updates across the PWM generator blocks. Extended PWM synchronization of timer/comparator updates across the PWM generator blocks. Interrupt status summary of the PWM generator blocks. Extended PWM fault handling, with multiple fault signals, programmable polarities, and filtering. PWM generators can be operated independently or synchronized with other generators. 124

141 ADC (Analog-to-Digital Converter) An analog-to-digital converter (ADC) is a peripheral that converts a continuous analog voltage to a discrete digital number. The Stellaris ADC module features 10-bit conversion resolution and supports16 input channels plus an internal temperature sensor. Four buffered sample sequencers allow rapid sampling of up to 16 analog input sources without controller intervention. Each sample sequencer provides flexible programming with fully configurable input source, trigger events, interrupt generation, and sequencer priority. Each ADC module has a digital comparator function that allows the conversion value to be diverted to a comparison unit that provides eight digital comparators. The LM3S9B96 microcontroller provides two ADC modules with the following features: 16 shared analog input channels Single-ended and differential-input configurations On-chip internal temperature sensor Maximum sample rate of one million samples/second Optional phase shift in sample time programmable from 22.5º to 337.5º Four programmable sample conversion sequencers from one to eight entries long, with corresponding conversion result FIFOs Flexible trigger control Controller (software) Timers 125

142 Analog Comparators PWM GPIO Hardware averaging of up to 64 samples Digital comparison unit providing eight digital comparators Converter uses an internal 3-V reference or an external reference Power and ground for the analog circuitry is separate from the digital power and ground Efficient transfers using Micro Direct Memory Access Controller (μdma) Dedicated channel for each sample sequencer ADC module uses burst requests for DMA The microcontroller contains two identical Analog-to-Digital Converter modules. These two modules, ADC0 and ADC1, share the same 16 analog input channels. Each ADC module operates independently and can therefore execute different sample sequences, sample any of the analog input channels at any time, and generate different interrupts and triggers. Figure 3.6 shows how the two modules are connected to analog inputs and the system bus. Fig 3.6 Block diagram of ADC 126

143 JTAG and ARM Serial Wire Debug: The Joint Test Action Group (JTAG) port is an IEEE standard that defines a Test Access Port and Boundary Scan Architecture for digital integrated circuits and provides a standardized serial interface for controlling the associated test logic. The TAP, Instruction Register (IR), and Data Registers (DR) can be used to test the interconnections of assembled printed circuit boards and obtain manufacturing information on the components. The JTAG Port also provides a means of accessing and controlling design-for-test features such as I/O pin observation and control, scan testing, and debugging. Texas Instruments replaces the ARM SW- DP and JTAG-DP with the ARM Serial Wire JTAG Debug Port(SWJ-DP) interface. The SWJ-DP interface combines the SWD and JTAG debug ports into one module providing all the normal JTAG debug and test functionality plus realtime access to system memory without halting the core or requiring any target resident code. The SWJ-DP interface has the following features: IEEE compatible Test Access Port (TAP) controller Four-bit Instruction Register (IR) chain for storing JTAG instructions IEEE standard instructions: BYPASS, IDCODE, SAMPLE/PRELOAD, EXTEST and INTEST ARM additional instructions: APACC, DPACC and ABORT Integrated ARM Serial Wire Debug (SWD) Serial Wire JTAG Debug Port (SWJ-DP). Flash Patch and Breakpoint (FPB) unit for implementing breakpoints. 127

144 Data Watch point and Trace (DWT) unit for implementing watch points, trigger resources, and system profiling. Instrumentation Trace Macro-cell (ITM) for support of printf style debugging. Trace Port Interface Unit (TPIU) for bridging to a Trace Port Analyzer. The JTAG port is comprised of four pins: TCK, TMS, TDI, and TDO. Data is transmitted serially into the controller on TDI and out of the controller on TDO. The interpretation of this data is dependent on the current state of the TAP controller. For detailed information on the operation of the JTAG port and TAP controller, please refer to the IEEE Standard Test Access Port and Boundary-Scan Architecture. The JTAG controller works with the ARM JTAG controller built into the Cortex- M3 core by multiplexing the TDO outputs from both JTAG controllers. ARM JTAG instructions select the ARMTDO output while JTAG instructions select the TDO output. The multiplexer is controlled by the JTAG controller, which has comprehensive programming for the ARM, and unimplemented JTAG instructions [13].JTAG block diagram is shown at figure RTC (Real Time Clock) The Real Time Clock (RTC) is a set of counters for measuring time when system power on, and optionally when it is off. It uses little power in Power-down mode. On thelpc214x, the RTC can be clocked by a separate KHz oscillator or 128

145 by a programmable pre-scale divider based on the APB clock. Also, the RTC is powered by its own power supply pin, VBAT, which can be connected to a battery or to the same 3.3Vsupply used by the rest of the device [15]. It has the following features: Measures the passage of time to maintain a calendar and clock. Ultra Low Power design to support battery powered systems. Provides Seconds, Minutes, Hours, Day of Month, Month, Year, Day of Week, and Day of Year. Dedicated 32 khz oscillator or programmable pre-scalar from APB clock. Dedicated power supply pin can be connected to a battery or to the main 3.3 V. Figure 3.6 JTAG block diagram 129

146 The RTC includes a number of registers. The address space is split into four sections by functionality. The first eight addresses are the Miscellaneous Register Group. The second set of eight locations is the Time Counter Group. The third set of eight locations contains the Alarm Register Group. The remaining registers control the Reference Clock Divider. The Real Time Clock includes the register shown in Table 3.1. Detailed descriptions of the registers follow. Table 3.1 Real Time Clock (RTC) register map Name Size Description Access Address ILR 2 Interrupt Location Register R/W 0xE CTC 15 Clock Tick Counter RO 0xE CCR 4 Clock Control Register R/W 0xE CIIR 8 Counter Increment Interrupt Register R/W 0xE C CTIME0 32 Consolidated Time Register 0 RO 0xE CTIME1 32 Consolidated Time Register 1 RO 0xE CTIME2 32 Consolidated Time Register 2 RO 0xE C SEC 6 Seconds Counter R/W 0xE MIN 6 Minutes Register R/W 0xE HOUR 5 Hours Register R/W 0xE DOM 5 Day of Month Register R/W 0xE C DOW 3 Day of Week Register R/W 0xE DOY 9 Day of Year Register R/W 0xE MONTH 4 Months Register R/W 0xE YEAR 12 Years Register R/W 0xE C ALSEC 6 Alarm value for Seconds R/W 0xE ALMIN 6 Alarm value for Minutes R/W 0xE ALHOUR 5 Alarm value for Seconds R/W 0xE ALDOM 5 Alarm value for Day of Month R/W 0xE C ALDOW 3 Alarm value for Day of Week R/W 0xE ALDOY 9 Alarm value for Day of Year R/W 0xE ALMON 4 Alarm value for Months R/W 0xE ALYEAR 12 Alarm value for Year R/W 0xE C PREINT 13 Prescaler value, integer portion R/W 0xE PREFRAC 15 Prescaler value, integer portion R/W 0xE

147 In the present study, the position of the sun is found out from the relational table designed with respect to time and position of the sun. To evaluate the position of the sun from the table, time has to be obtained. For that RTC is enabled and read time continuously from RTC registers and extracts the position from the table to produce appropriate PWM signal to rotate the motor. 131

148 REFERENCES [1] Michael Barr. "Embedded Systems Glossary". Neutrino Technical Library. Retrieved [2] Heath, Steve (2003). Embedded systems design. EDN series for design engineers (2 ed.). Newnes. p. 2.ISBN "An embedded system is a microprocessor based system that is built to control a function or a range of functions." [3] Michael Barr; Anthony J. Massa (2006). "Introduction". Programming embedded systems: with C and GNU development tools. O'Reilly. pp ISBN [4] Taha, W, Hudak, P. and Zahnyong, W, Directions in functional programming of real Time applications, EMSOFT, pp , [5] Jose L. Ayala, Marisa Lopez-Vallejo, David Atienza, Praveen Raghuvan, Energy-aware compilation and hardware design for VLIW embedded systems, International Journal of Embedded Systems, vol. 3, No. ½ pp , [6] Albert Mo Kim Cheng, A survey of formal verification methods and tools for embedded and real-time systems, International Journal of Embedded Systems, vol. 2, No. ¾ pp , [7] Philip Koopman, Challenges in Embedded Systems Research & Education Electrical &Computer ENGINEERING [8] Embedded technology challenges, [9] About embedded technology chanllenges, [10] Sanz, R. Arzen, K.-E. Univ. Politenica de Madrid, Spain, Trends in software and control, vol. 23, Issue: 3, pp , June [11] Marko Wolf, Andre Weimerskirch, and Thomas Wollinger, State of the Art: embedding Security in Vehicles, EURASIP Journal of Embedded Systems, pp. 16, [12] LuiSha, Chang-Gun Lee, Real-time virtual machines for avionics software migration, International Journal of Embedded Systems, vol. 2, No. 3/4, pp ,

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150 [24] Maya Gokhalae and Paul Graham Dynamic reconfiguration for management of radiation-induced faults in FPGAs, Int. J. embedded Systems, vol. 2, No. ½, pp , [25] P. Magarshack, Improving Soc Design Quality Through a Reproducible Design Flow, IEEE Design & Test of Computers, pp , [26] Radojka Krneta, Miroslav Bjekic, Aleksandar Peilic, Snezana M. Ragicevic, Dynamic analysis of temperature and heat gains in classrooms with different type of windows,thermal science, vol. 19, Issue 4, pp , [27] F. Cordoba-Mintiel, S.F. Hernandez-Machuca & D. Hernanadez-ventura, Micro controller brid network for distributed instrumentation, Journal of applied research and technology, August, vol. 2, pp , [28] Buakwtm, Rising Tides, Dr. Dobb s Journal vol. 346, [29] J. Shandle, More for Less: Stable Future for 8-bit Microcontrollers, Tech On Line, [30] M. Le, 8-bit microcontrollers: still going,ee Times, [31] Li, Aicheng, Li, Jianlong, Yang, Qi, Gang, Chengcheng, Research of real-time image acquisition system based on ARM 7 for agricultural environmental monitoring, IEEE international Symposium vol June [32] IEEE Standard for Floating-Point Arithmetic, IEEE international Symposium vol: IEEE Std [33] Smiarowski, A, Jr. UNIX-based robotic controller architecture, IEEE international Symposium vol Mar [34] Yinping Jiang, Research of roots flow meter based on ARM Cortex- M3, IEEE international Symposium vol /cecnet

151 Chapter 4 DESIGN AND DEVELOPMENT OF ETHERNET BASED D.C. MOTOR SPEED CONTROL BY USING ARM Cortex PROCESSOR

152 4.1 Introduction The electrical motors used commonly in industrial applications are different from each other as structure. It is used to convert the electrical energy, supplied by the controller to mechanical energy to move the load. There are really two types of motors, AC and DC. The basic principles are alike for both. DC motor is a power actuator device, which converts direct current electrical energy into rotational mechanical energy. Because of their versatile features such as high torque, speed controllability over a wide range, portability, well-behaved speed-torque characteristics, and adaptability to various types of control methods, DC motors are still widely used in various industry applications, including steel rolling mills, electric trains, robotic manipulators, machine tools, picking, placing, packing the objects etc., [1-4]. DC motor fundamental details are shown Figure 4.1. Figure 4.1 DC motor fundamental Diagram. 135

153 PID logic controllers have some advantages compared to other controllers such as the simplicity of control, and ability to design control system without the need for accurate and precise mathematical model of a complex and non-linear system [5-7]. Hence, the present research towards design and development of PID logic controllers for DC motor speed application. A variant of the wound field DC motor is the universal motor. The first commutator-type direct-current electric motor capable of a practical application was invented by the British scientist William Sturgeon in Following Sturgeon s work, a commutator-type direct-current electric motor made with the intention of commercial use was built by the American Thomas Davenport and patented in Although several of these motors were built and used to operate equipment such as a printing press, due to the high cost of primary battery power, the motors were commercially unsuccessful and Davenport went bankrupt. Several inventors followed Sturgeon in the development of DC motors but all encountered the same cost issues with primary battery power. No electricity distribution had been developed at the time. Like sturgeon s motor, there was no practical commercial market for these motors. The DC motors are widely used in the variable speed applications due to the ease of speed control. In closed-loop system, the speed can be maintained constant by adjusting the motor terminal voltage. Smaller DC motors operate at lower voltages, which make them easier to interface with control electronics. DC motors are commonly used where precise speed control is necessary, as in computer disk drives or in video cassette recorders, the spindles within CD, CD-ROM drives, 136

154 and mechanisms within office products such as fans, laser printers and photocopiers etc., The detailed discussion of DC motors is presented in many books [1, 8-9]. The main advantage of the DC motor is low maintenance, High efficiency. Since the DC motors are used especially in speed maintaining applications, the speed is one of the important process parameter that is to be monitored and controlled in industry. 4.2 Principle The principle and block diagram of the ARM processor based DC motor speed control system is illustrated in Figure 4.2. The block diagram consists of an ARM processor based LM3S9B96 controller, DC motor with optical sensing unit, F/V converter and a personal computer. The optical encoder senses the speed of the motor and converts it into a train of TTL compatible pulses. Frequency is generated with proportional to the speed of the motor. This frequency is converted into proportional voltage by F/V converter. Microcontroller acquires the voltage through built in 12-bit A/D converter. This voltage in digital form is converted back to corresponding frequency by the equation V out = (F in *2.09)*(R l /R s )*(R t C t ), where Finis the frequency of the signal generated from optical encoder, V out is the measured voltage of F/V converter, this frequency is converted into speed in rpm by the equation. Speed = (Frequency *60 seconds) rpm. = (Frequency * 60) rpm 137

155 For the optical encoder used, one pulse is generated for one complete revolution, which is converting into voltage by using the frequency to voltage driver circuit [IC LM331] to be compatible with microcontroller in-built ADC. The measured motor speed is compared with the set value to obtain the error and this error along with change-in-error is applied to the PD, PI and PID algorithms. The controller produces the control action according to the error. The microcontroller then applies this control action, in the form of voltage, to the motor through PWM generator. The ON time of PWM wave varies with respect to error signal. If the signal loss/gain is more, then ON time will be more and vice-versa. Hence, the power applied to the motor through actuator will vary with PWM wave. This procedure is repeated till the motor reaches the desired speed. Thus the motor speed is controlled at the desired value. The details of individual blocks of the block diagram are discussed in the subsequent sections. 4.3 Hardware Features: The complete hardware of the system including LM3S9B96 development board, Driver circuit, DC motor with speed sensor, Relay Circuit and a personal computer are designed and fabricated indigenously in the present study. The system architecture of the proposed ARM based speed control system is shown in Figure 4.2. The following sections describe the hardware features of the DC motor speed control system. 138

156 +12V ARM Cortex Processor LM3S9B96 Input / Output Unit PWM Circuit Driver Circuit Personal Computer Key Board Ethernet controlling Unit PID JTAG +5V Shaft ADC F/V Circuit Figure 4.2 : Block Diagram of the ARM Cortex processor based DC motor speed control system Disc Speed sensor +5V 139

157 4.3.1 DC motor and Speed Sensing Unit: The DC motor, its specifications and the speed sensing unit used in the present study are discussed in this section. Most electric motors, in which electromagnetic torque is utilized, are based on the energy conversion from electrical to magnetic then to mechanical energy [2]. An indispensable component of the control system is the actuator. The actuator is the first system component to actually move, converting electrical energy into mechanical motion. The most common type of actuator is the electric motor, which is electromechanical energy converter, converting electrical energy in to mechanical energy. Motors are classified as either DC or AC, depending on the type of power they use. Though the DC motors are much expensive than AC motors, they become prevalent in machinery control because their speed and torque are easy to control with the simple electronics [10-12]. DC motor features speed controllability over a wide range and even direction of rotation can be changed at any time to meet new conditions. Smaller DC motors commonly operate at lower voltages (for example, 5V disk drive motor), which make them easier to interface with control electronics. The specifications of the DC motor used in the present study are given in the Table 4.1. The photograph of DC motor and Speed Sensing Unit is shown in Figure 4.3. Table 4.1 The specifications of DC motor Description Rated voltage Rated current Maximum speed Torque Weight Value 12 V DC 500 ma 3000 RPM 50 gm-cm 150 g 140

158 Optical encoder is one of the widely used speed sensors. This non-contacting type sensor consists of an optical source on one side, and a detector on the other side. The slotted disc, connected to the shaft of DC motor, rotates between the optical source and detector, thereby producing optical chopping. The result of this is an output of pulses with a rate proportional to speed and the number of slots on the disk. The optical source is usually an LED or an infrared emitting diode. The detector can be a photo responsive semiconductor device, such as a photodiode, a phototransistor, a photo Darlington, or a photo FET. The advantage of these Optointerrupters is that, fine speed control resolution is easily realized by merely introducing a number of slots on the disk. These Opto-interrupters are called as slotted optical switches. In the present study, the optical encoder is designed with the slotted optical switch with one hole contains on disc for measuring the speed of the DC motor. The disc is connected to the shaft of the DC motor. As the DC motor runs, the disc rotates between the optical source and detector of slotted optical switch. During the rotation of the disc, whenever hole appears between the optical source and the detector, the light is chopped periodically. Hence, a train of TTL compatible pulses whose frequency is proportional to the speed of the DC motor is generated. Since, a disc with single slot is used; one pulse is generated for one revolution. These pulses are fed to the F/V converter, to convert the frequency of the pulses into the corresponding analog voltage. 141

159 Figure 4.3 Photograph of DC motor and Speed Sensing Unit ARM Cortex Processor (LM3S9B96) board Unit: ARM processor is the main backbone of the control system in the present study. This establishes communication with external devices to exchange information with remote devices, generates PWM signal to control actuators and A/D conversion operation to convert analog signals in to digital form for processing. The architecture of ARM processor is discussed in chapter 3. It plays major role in data acquisition, measurement, display, record and generates control action to the process under investigation. The processor is used to measure voltages and control the DC motor speed. ARM Cortex LM3S9B96application specific board has been used. It is a standalone board for LM3S9B96microcontroller. It has 60MHz crystal for system clock and 32 KHz crystal for RTC. It has power on reset circuit with MCP130T 142

160 brownout monitoring chip and power decoupling capacitors. All resources inside LM3S9B96 are quite perfect, and it is the most suitable to learn and study because, if user can learn and understand the applications of all resources inside MCU well, the user can modify, apply and develop many excellent applications in the future. Hardware system of LM3S9B96 includes the necessary devices within only one MCU such as USB, ADC, DAC, Timer/Counter, PWM, Capture, I2C, SPI, UART, and etc[13-14].the photograph of ARM Cortex board is shown in Figure 4.4. In the present study, UART, PWM, ADC and ethernet features of the controller are enabled by calling the corresponding API system calls, control signals will be received from the remote computer through ethernet channel of the controller. According to these inputs PWM signals will be generated to control the speed of the DC motor. The speed of the motor is measured using F/V converter, whose output is connected to the controllers inbuilt 12 bit A/D converter to provide digital equivalent voltage. After that the samples are sent to personal computer (PC) through ethernet communication, the performance of the system is verified with PID algorithm. The Ethernet data transmission has become very important to the overall computing strategy in industrial and commercial applications. In computer, the sample values are read by using ethernet communication by implementing TCP/IP sockets in C# programming language in ASP.Net. The samples are stored in MS-Access data base file, by using file management library functions in.net frame work in PC. Plots are also drawn with the same signals for user flexibility, easy to understand and observation. The speed of the DC 143

161 motor is measured and compared with given input (investigated) on personal computer in the form of graphical representation with the help of ASP.NET software. The performance of the system is tested with various input signals. Figure 4.4: Photograph of ARM Cortex (LM3S9B96) board Unit Pulse Width Modulation (PWM) The speed of DC motor depends on the current through its armature coil. With a field coil motor the speed can be changed by either varying the armature current or the field current. Generally it is the armature current that is varied. Thus, speed control can be achieved by controlling the voltage applied to the armature. Since in the present study, the control signals for DC motor speed control are emanated from the computer, in such cases a technique known as pulse width modulation (PWM) is generally used. This basically involves taking a constant DC supply voltage and chopping it so that the average value is varied. Hence PWM is used to control the average voltage applied to the armature. 144

162 PWM wave is an analog signal that switches between two predefined limits. The switching interval of the PWM, controlled by the reference signal (D/A converter output), determines the average power delivered to the motor through actuator. As the voltage increases, width (duty cycle) of the PWM increases which in turn increases the speed of DC motor and as the voltage decreases, width of PWM decreases which in turn decreases the speed of DC motor. The PWM signal is proportional to the reference signal, which in turn is proportional to the output of computer [20]. LM3S9B96 controller is having PWM as an internal feature, which will be enabled by calling corresponding API system calls; the pulse width is controlled by setting the on-off time. Pulse-width modulation (PWM), or pulse-duration modulation (PDM), is a commonly used technique for controlling power to internal electrical devices, made practical by modern electronic power switches [17, 15]. In the present study, DC motor speed control PWM duty cycle signals are emanated from the remote computer; in this study pulse width modulation (PWM) technique is initiated by calling API system calls. This basically involves taking a constant DC supply voltage and chopping it so that the average value is varied. Hence PWM is used to control the average voltage applied to the armature. PWM wave is an analog signal that switches between two predefined limits. The switching interval of the PWM, controlled by the reference signal, determines the 145

163 average power delivered to the motor trough actuator. By varying the duty cycle of the PWM the speed of the motor is controlled. JTAG Interface: ARM Cortex-M3 is having JTAG as an inbuilt feature to debug the application program and to burn the.exe in to the controller PROM/EEPROM/SDROM. In the present study JTAG is used to debug the application programming code statement by statement by setting the set points and break points at different routines to track software bugs and hardware response signals and to monitor processor registers status and contents. Ethernet Controller: ARM Cortex-M3 consist Ethernet as a built-in feature to establish communication with external world using TCP/IP protocol suite, through LAN connections. The Ethernet Controller conforms to IEEE specifications and fully supports 10BASE-T and 100BASE-TX standards. In the present study, Ethernet is used to establish communication with remote computer for receiving the control signals for the motor to control and send back the speed of the motor to personal computer for users to visualize. Analog to Digital Converter (ADC) ADC is an internal feature, which uses sampling to convert a continuous quantity to a discrete time representation in digital form. Basic clocking for the A/D converters is provided by the APB clock. A programmable divider is included in 146

164 each converter. An ADC may also provide an isolated measurement such as an electronic device that converts an input analog voltage or current to a digital number proportional to the magnitude of the voltage or current[19]. Since in the present study, the F/V converter output voltage obtained is analog signals, which cannot be processed with controller so that before processing it has to be converted into digital form. For that these signals are given to the ADC inputs of the controller which will convert the analog input signals in to equality digital signals. The converted digital signals are sent to the remote computer to visualize on GUI window designed on personal computer and used by the controller to compute the error and rectify Frequency to Voltage Converter As the sensing unit produces train of pulses with proportional to the number of rotations of the motor, those pulses cannot capture and measure through the controller accurately even as an interrupt signal as those pulses are more. A dedicated circuit (frequency to voltage converter) has been designed to measure the pulses produced by the sensing unit and converts in to equivalent voltage levels. Frequency to voltage converter is designed using LM331, a monolithic integrated circuit from National Semiconductor available on 8-pin DIP. To converts the frequency of the pulses coming from optical encoder into corresponding analog voltage [20-21]. LM331 provides an output voltage proportional to the input frequency and provides zero output at zero input frequency. The detailed F/V 147

165 converter circuit diagram is shown in Figure 4.5. The LM331 includes three basic components: an input amplifier with built-in hysteris is a charge pump frequency to voltage converter, and a versatile operational amplifier/comparator with an uncommitted output transistor. In this configuration, the first stage of operation is a differential amplifier driving a positive feedback flip-flop circuit. A frequency signal is applied to the input of the charge pump at pin 1. Following the input stage is the charge pump. The charge pump converts the frequency into DC voltage. To do this, one timer capacitor, one output resistor, and an integrating or filter capacitor are required. The voltage appearing at pin1 is the equivalent of frequency input provided at pin 6. Photograph of frequency to voltage converter circuit is shown in Figure Vs=15V DC 10K 10K 6.8K Rt 470µF 68K 7 6 LM331LM µF Ct Fin Rs 12K µF 100K Rl Vout Figure 4.5 Frequency to Voltage Converter circuit diagram 148

166 Figure 4.6: Photograph of frequency to voltage converter and Driver circuit Driver Circuit Microcontroller cannot be connected to a motor directly because microcontroller will not give sufficient current to drive the DC motors. Motor driver is a current enhancing device, it can also act as a Switching Device. Motor driver take the input signals from microcontroller and generate corresponding output for motor. The driver circuit diagram is shown in Figure 4.7. L239D is a motor driver IC that can drive two motor simultaneously. It is a dual H-bridge motor driver IC. One H-bridge is capable to drive a dc motor in bidirectional. L293D IC is a current enhancing IC as the output from the sensor is not able to drive motors itself so L293D is used for this purpose. L293D is a

167 pin IC having two enables pins which should always be remain high to enable both the H-bridges. Photograph of driver circuit is shown in Figure 4.6. Figure 4.7 Driver Circuit diagram Since in the present study, the output of the PWM signal is connected to the DC motor through the L239D driver circuit to control the motor, the PWM signal is passed via driver circuit to protect the controller board from the back EMF produced by the DC motor while switching between forward and reverse directions. The driver circuit will also increase the strength of the controller signal which cannot drive the motor, so that the motor can be energized Personal Computer: Here computer is the most important and integral part of the entire control system as it plays a vital role in data acquisition, display, record, and generate graphs. The 150

168 predominant interfaces between personal computer and microcontroller are LPT, RS232 and Ethernet. In this contest Ethernet communication is implemented to transfer data to and from controller to PC to display and store in database. Hence, a personal computer with the following configuration is employed in the present study for graphical user interface design and recording data for future analysis. Pentium IV Processor with2.0ghz frequency 1GB RAM 20 GB HDD RS232 compatible Serial Ports Besides above-mentioned hardware configuration computer is loaded with windows XP operating system and Visual Studios2010. For providing flexibility and storability GUI software has been developed in Visual Studios2010 using XML and C# programming, which will provide a graphical visualization for the user to monitor the status of the signal and stores the data for future study. In the present study motor speed is measured and displayed on GUI window. Photograph of complete view of experimental setup and working model is shown in Figure

169 Figure 4.8 Photograph of complete view of experimental setup and working model. 4.4 Software Features The software developed for DC motor speed control system provides the user interface to enter the set point, tune controller parameter and display the PID logic controller parameter values and key variables like measured speed, set point speed in RPM etc., on personal computer. The complete software is divided into three major parts viz., measurement, control and display. The software first declares and initializes all program variables, constants, and functions. The software then enters the measurement control programs and displays them. In measurement, the software acquires the voltage proportional to the speed of the DC motor through A/D convertor, substitutes it into the linear equation of the curve between frequency and voltage to find the frequency and hence the actual speed in RPM, and displays it on the computer. 152

170 On other hand in control, the software computes the error and change-in-error and applies them to one of the control algorithm PID to generate the control action. Since control action is a digital value, the software sends through D/A converter to get it converted to analog voltage to enable outside world understand the analog signal as the actuating signal for final control element. The following section describes the software development for Proportional plus Integral plus Derivative [PID] logic for DC motor speed control system and implementation of PID logic controllers in embedded C language in detail. The necessary algorithm, flowchart and embedded C language programs, software features and C-cross compiler are presented and thoroughly discussed. Embedded C Compiler: There are various cross compilers available in the present market like Keil-IDE, MPLab IDE and CCS (Code Composer Studio)-IDE, Silicon-Laboratory IDE etc. These development tools facilitate users to efficiently/flexibly develop and debug application code. The ANSI C compiler and standard libraries are altered or enhanced to address the particularities of an embedded target processor. The C programming language is a more popular general-purpose programming language that provides code efficient elements of structured programming and a rich set of operations for developing applications from core level by accessing the addresses of the memory and devices using pointers. Its generality combined with its absence of restrictions, makes a convenient and effective programming 153

171 solutions for a wide variety of software tasks. Many applications can be solved more easily and efficiently with C than with other more specialized languages. The compiler offers a number of control directives, which are used to control compilations. Directives are composed of one or more letters or digits and, unless otherwise specified, can be specified after the filename on the command line or within a source file. The program consists of all control directives such as symbols, code and debug. The debug directive instructs the compiler to include debugging information in the object file. By default debugging, information is executed from generated object file. Debug information is necessary for the symbolic testing of programs. This information contains both global and local variable definition and their addresses, as well as function names and their line numbers. Debug information contained in each object module remains valid through the link/locate procedure. This information may be used by the micro version4 debugging or by any of the Intelcompatible emulators. In the present study, keil-cross compiler IDE is used to develop C code of DC motor speed control. This is a complete implementation of the American National Standard Institute (ANSI) standard for the C language. The Keil is a ground-up implementation dedicated to generating extremely fast and compact code for the various microcontroller families. The Keil IDE supports the microcontrollers such as Atmel, Philips, Motorola, Dolphin, Cygnal, Analog devices, Maxim, and Intel etc. 154

172 To provide the flexibility and to reduce complexity, modularization programming method has been implemented. Modularizing the whole software, the work of designing, testing, debugging, maintenance and so on becomes flexible [25]. In program, following two points are considered: one is distributing system resources in reason, which include ROM, RAM, interrupt sources and so on; another is enhancing its universality and anti-jamming ability. For the writing of application program, Keil cross compiler and Embedded C language are combined. Keil IDE which is based on Windows platform supports all Intel and ARM controller libraries to develop applications. Keil is an intact development environment for embedded application software development, compile and debug. It is designed by Keil Company as the special C compiling system for designing embedded system based on ARM microprocessor. It has high speed of compiling and mutual debugging characteristic of real time environment. Keil includes library of all bottom I/O driver functions, which has lightened the load of software development greatly. It has real-time multitask kernel and can offer TCP/IP programming of SOCKET grade and support various kinds of network protocols (such as HTTP, FTP, SMTP, PPP). Keil IDE is compatible with C, assembly and combination of assembly and C code. It supports debugger for testing the application program when it is running on the target board. In another word, it can compile designed program to produce a mapping file which can dump in to target board. When running on PC, Keil can 155

173 compile memorizer directly and accede to BIOS during compiling user program. With the development of network technology, TCP/IP protocol has been written into the embedded system, as a result, embedded system becomes to embedded web system, corresponding to that controller of embedded system turns into a miniature network server. Until then the seamless link of bottom equipment with Internet becomes true and the remote monitoring is realized indeed. LM3S9B96 controller has installed TCP/IP protocol, so it is easy to realize connecting with internet. The software environment of this work consists of primarily Embedded C and ASP.NET. Web server application is developed by implementing a Transmission Control Protocol/ Internet Protocol (TCP/IP) using embedded C programming in Keil Cross Compiler IDE (Integrated Development Environment), and ported into the ARM Cortex-M3 processor to establish communication between the LM3S9B96 controller interfaced with DC motor speed control unit and the remote client PC (Personal Computer) on which the GUI is running to facilitate the user to view the actual speed of the motor and passing control signals to the controller. This Embedded C program initiates and performs all data exchange to and from PC, including encoding/decoding of sensory data, which is to be sent to a remote client, in TCP frame format, which is received from the remote client. The GUI (Graphical User Interface) Web pages which runs on a remote client computer are implemented by hypertext markup language (HTML), back ground controls and sockets are implemented using C# programming in ASP.NET framework; this GUI allows remote clients using any web browsing compatible 156

174 operating system to interact with experiment test-bed, and the database is designed using SQL Server2005 and link with GUI interface, which will stores the motor actual speed with given speed continuously in to the database for feature analysis. This recorded data viewed in the grid view format which provides a way to view the data from the database by using to periods entered by the user. 4.5 Experimental Implementation The experimental implementation of the PID logic speed controller for DC motor is accomplished by developing the necessary software for the hardware. The control algorithms are realized on LM3S9B96 ARM Cortex-M3 controller using embedded C language in Keil IDE. The following discussion epitomizes the design methodology of PID logic controller for the proposed system. The controller provides the reasonable tracking of the motor speed to a pre-specified reference speed [1]. The sensing opto-coupler and DC motor arranged properly and it is interfaced with microcontroller. The microcontroller acquires the voltage proportional to the speed of the motor through built-in 12-bit A/D convertor and substitutes a well calibrated equation to evaluate the actual speed of the motor. The frequency versus voltage plot is fitted to the following linear equation using ASP.NET. V out = (F in *2.09)*(R l /R s )*(R t C t ), where V is the voltage acquired by the controller. From the above equation, the speed in RPM is calculated as follows, 157

175 Speed = (Frequency *60 seconds) rpm. = (Frequency * 60) rpm where P represents the number of pulses per one rotation of disk. After evaluation of the speed, the ARM processor determines the error (reference speed measured speed) and change in error (present error previous error), and applies to PID control algorithm. The necessary variables are used in embedded C language to calculate the error and implemented P, PI, and PID logics with help of Keil cross compiler IDE. The complete circuit schematic of the ARM processor based DC motor speed control system is shown in Figure 4.9. Design of PID speed controller The basics of the P, P1, and PID are controller area already discussed in chapter 1. The designing of mathematical expression for PID controls is presented below. One of the most powerful controller operation which combines the proportional, integral and derivate mode. This system can be used for virtually for any process condition. The analytical expression of PID logic for speed control is P = Kp[ep + Ki epdt + Kddep/dt] The well known the mathematical expression for the velocity type PID controller is given as [3-7]. Vn = Vn-1 + Kp(en en-1) + KienT + Kd/T [(en 2en-1 + en-2)] The above equation representing the velocity algorithm is modified into improved PID difference equation by using trapezoidal rule and interpolation technique that is given by the following equation. 158

176 159

177 Vn = Vn-1 + Kp(en en-1) + Ki(en + en-1)/2t + Kd/6T [(en 2en-1 6en-2 + 2en-3+ 2en-4)] Where Kp, Ki and Kd are proportional, integral and derivative constants respectively Vn-1 is the previous control action en, en-1 are the present and previous errors respectively en-2, en-3, and en-4 are previous to previous errors In the present application, the best-tuned Kp, Ki, and Kd values are found to be equal to 68.8, 0.41, and 5.0 respectively and cycle time T is equal to Flowchart of the PID The flowchart is drawn that is self-explanatory and gives the complete idea of how computer sequentially does the different steps involved in measurement and control. The flowchart of ARM cortex based proportional control for DC motor speed control system in shown in Figure

178 START P computation (Vn) Initialization of all variables and functions of UART T0, 12-bit ADC, ethernet and system clock If Vn>4096 send 0x0fff, else if Vn<0 send 0x0000 to DAC A Give set point, P, I, D values on PC Apply control action to the actuator through 12-bit D/C converter Decrementing counter If set key presses No B Yes User enters to set point speed, Kpand press the start key Read the current speed of the DC motor from F/V convertor through 12 A/D convertor B No If Counter = 0 Yes Sent stored speed data to personal Computer through ethernet A Computing the speed in RPM frequency = (V/2.09)*(Rs/Ri)*(1/RtCt), and speed =(Frequency*60)/12 in RPM and speed values stored Compute the error (en = Set point speed measured) Figure 4.10 Flowchart of the ARM Cortex processor based Proportional Integral Derivative Program [PID] for logic DC Motor speed control system 161

179 4.7 Results and Discussion The design and development and fabrication of ARM cortex-m3 controller based PID logic controller for DC motor speed controller have been discussed. Process parameters considered are DC motor position, speed control application. The complete hardware software features of DC motor speed control system have been thoroughly discussed. The experimental studies are carried out to evaluate the performance of designed controllers under varies test conditions. The present chapter discusses the results and impartment conclusions that are drawn from the experimental observation and feature scope for the work. The DC motor is controlled and tested by PID controller, which is designed for the desired speed of 1000RPM. The efficiency of the proposed controllers for the speed control of DC motor is evaluated by applying several tests over a wide range operations conditions. The performance of the proposed controllers is studied for varies studies including step input, step variations and set point variations response of PID controller. The above mentioned study for proportional Integral Derivative logic controller [PID] is shown in the Figure Similarly the studies of PID are presented in Figures4.12, 4.13 and From the graph it is observed that the PID has the best transient and study state response. The step response of DC motor was investigated by applying step input corresponding speed reference of 1000 RPM. Figure 4.11 shows the starting of the motor from stand still to the reference speed. 162

180 Figure 4.11 Step response of DC motor speed control system for set point 1000 RPM using PID control. Figure 4.12: DC motor speed control system using PID logic for 800, 1000 and 1200 RPM step variations. 163

181 Figure 4.13: DC motor speed control system using PID logic for 1000, 800 and 1200 RPM step variations. Figure 4.14 DC motor speed control system using PID logic for 600, 800 and 1000 RPM step variations. 164

182 The performance indices of PID controller (in terms of settling time, overshoot, under shoot, steady state error) are mentioned in Table 4.2. It summarizes the results obtained from the tests performed. It can be seen from the table that the PID has best performance. It exhibits a very fast response and very less overshoot, undershoots, with negligible steady state error. From the table, it is obvious that settling time of PID is sec. Table 4.2 Results of the DC motor speed control system Controller Sampling interval (seconds) Maximum(RPM) Overshoot Undershoot Settling time (Seconds) Steady-state error (RPM) PID The present results are compared with the work of borojevic, D et al., M.K Refai, Tag Jainxinet al., Sukumar Kamalasadan et al., group [26-29] where they designed and implemented PID logic control for the speed control of DC motor. They carried out the experimental analysis of DC motor for rated desired speed, set point variations. On comparing the present results of PID with them, the present observes much better time domain response, higher stability, reduced hardware and low cost. It is found that embedded microcontroller chip is used for controlling DC motor speed, F/V, driver circuit and other necessary hardware circuit etc. By using LM3S9B96 ARM controller reduced almost all hardware circuit for controlling DC motor 165

183 speed. The microcontroller contains built-in facility of almost all peripherals. Due to this, the designing cost is reduced and the speed is also stabilized. In the present study, three controllers are used for the speed control of DC motor. The performance of P, PI and PID controller are compared in terms of settling and graphical representations. It is found that present results are similar to results of P. Bhaskar and Katte et al,.[30-31]. As an indigenously designed hardware and software are used in the present study, this gives low cost and better than the any existing commercial instruments. 166

184 REFERENCES [1] Laiw C. M., Shue R.Y., Chen H. C., and Chen S. C., Development of Linear brushless DC motor drive with robust position control, IEEE proceedings on Electric power application, vol. 148, pp , [2] Richard C. Dorf, and Robert H. Bishop, Modern Control Systems, Addison Wesley Longman Inc., England, 8 th ed., [3] Pierre Sicard, Kamal Al-Haddad and Yves Dube, DC Motor position control using sliding mode and disturbance estimator, IEEE Power Electronics specialist Conference PESC 89, vol. 1, pp , [4] M.M. Jamali et. al., A CAN based Real-Time Embedded System for DC Motor control, SAE [5] M. Barr, Pulse Width Modulation, Embedded Systems Programming, vol. 14, No. 10,pp , September [6] Bolognani S. and Zigliotto M., Fuzzy logic control of a switched reluctance motor drive, IEEE Transactions on Industry Applications, vol. 12, pp , [7] Comparison, Circuit Cellar Magazine, Issue 216, Bachiochi, p. 78, July [8] Thomas E. Kissell, industrial Electronics, Prentice Hall of India Pvt. Ltd., New Delhi, 3 rd ed., [9] Richard H. Engelmann and William H. Middendorf, Handbook of Electric Motors, Marcel Dekker Inc. New York,

185 [10] A.O. Smith, The AC s and DC s of Electric Motors. Retrieved on April2006. [11] [DC1] Christopher T. Kilian, Modern Control Technology Components and Systems, West Publishing Company, Minneapolis/St. Paull, [12] P. Bhaskar, Parvathi C.S., L. Shrimanth Sudheer, and A.B. Kulkarni, Computer based DC micro motor speed control system, J. Instrum. Soc. India, vol. 34, No, 4, pp , [13] Reference manual: LPC Single-chip 16-bit/32-bit microcontrollers. [14] Douglas V. Hall, Microprocessors and Interfacing: Programming and Hardware, Tata McGraw-Hill, 2 nd ed., [15] W. Bolton, Mechatronics: Electronic Control Systems in Mechanical and Electrical Engineering, Pearson Education, LPE, 3rd ed., [16] Lee J., On methods of improving performance of PI-type fuzzy logic controllers, IEEE Transactions on Fuzzy Systems, vol. 1, pp , November [17] Eli FLAXER, Multi Channels PWM Controller for Thermo electric Cooler Using a Programmable Logic Device and Lab-Windows CVI, Sensors & Transducers Journal, Vol. 96, Issue 9, September 2008, pp [18] Hong Wong and Vikram Kapila., Internet-Based Remote Control of a DC Motor using an Embedded Ethernet Microcontroller Department of Mechanical, Aerospace, and Manufacturing Engineering Polytechnic University, Brooklyn, NY

186 [19] National Semiconductor Applications Specific Analog Products, Data book, [20] Chandrasekhar T., Nagabhushan Raju K., Embedded Based DC Motor Speed Control System; Sensors & Transducers Journal, Vol. 121, Issue 10, October 2010, pp [21] Robert F. Coughlin, and Frederick F. Driscoll, Op-Amps and Linear Integrated Circuits, Prentice Hall of India, New Delhi, 8 th ed., [22] David A. Bell, Electronics devices and Circuits, Prentice Hall of India Pvt. Ltd, New Delphi, 3 rd ed., [23] R.G. Irvine, Operational amplifier characteristics and Applications, Prentice-Hall Inc, Engewood cliffs, N. J., 3 rd ed [24] Ramakanth A. Gayakwad, Op-Amps and Linear Integrated Circuits, Prentice Hall of India, 3 rd ed., [25] Zhang xiaohua, Chen hongjun, Mengfanwei. Embedded monitoring system based onrabbit2000. Monitoring technology, 2002, 52(6): [26] Borojevic, D., Garces, L., Lee, F.C. Performance comparison of variable structure controls with PI control for DC motor speed regulation, IEEE- IAS, pp , October [27] M. K. Refai Microprocessor based digital controller for DC motor speed control Butterworth & Co., vol. 10, ,

187 [28] Tang, Jianxin, Using personal computers as digital controllers for DC motor position and speed control ASEE Annu Conf Proc, pp , June [29] Sukumar Kamalasadan1; Hande, A. A PID controller for real-time DC motor speed control using the C505C microcontroller, International Conference on Computer Applications in Industry Engineering, pp , November [30] P. Bhaskar, Parvathi C.S., L. Shrimanth Sudheer and A.B. Kulkarni, Computer based DC micro motor speed control system J. Instrum, Soc., vol. 58, No. 1, pp , January [31] Nagabhushana Katte Design and Development of Computer based Fuzzy and Integrated Fuzzy Logic Controllers for Process Parameters, Ph. D. thesis, July

188 Chapter 5 APPLICATION OF DC MOTOR SPEED CONTROL FOR SOLAR POWER TRACKING SYSTEM

189 5.1 Introduction Radiant light and heat from the sun are the most powerful resources available on earth. So far the efficiency of generating power from solar energy is relatively low. Thus, increasing the efficiency of generating power of solar energy is very important [1-2]. In the past, solar cells have been hooked with fixed direction. They do not track the sun frequently and therefore, the efficiency of power generation is low. For example, the elevating angle of a solar cell for the largest volume of illumination in day time is 23.5 in southern countries. Since the fixedtype solar panel cannot obtain the maximum solar energy, the transformation efficiency of solar energy is limited. Many scholars have proposed different methods for tracking the sun [3-9]. Extracting useable electricity from the sun was made possible by the discovery of the photoelectric mechanism and subsequent development of the solar cell, a semi conductive material that converts visible light into a direct power. By using solar arrays, a series of solar cells electrically connected, a DC voltage is generated which is physically used on a load. Solar arrays or panels are being used increasingly as efficiencies reach higher levels, and are especially popular in remote areas where placement of electricity lines is not economically viable. 171

190 Motors have already become an important drive configuration for many applications across a wide range of powers and speeds. The ease of control and excellent performance of the motors is ensuring that the number of applications using them will continue to grow in the foreseeable future. Before examining the functionality of a drive, one must understand the basic operation of the motor. It is used to convert the electrical energy, supplied by the controller to mechanical energy to move the load. There are really two types of motors, AC and DC. The basic principles are alike for both. Magnetism is the basis for all electric motor operation. It produces the force required to run the motor. There are two types of magnets the permanent magnet and the electro magnet. Electro magnets have the advantage over permanent magnet in that the magnetic field can be made stronger. Also the polarity of the electro magnet can easily be reversed. The construction of an electro magnet is simple. When current passes through a coil of wire, a magnetic field is produced. DC motors are still widely used in various industry applications, including steel rolling mills, electric trains, robotic manipulators, machine tools, picking, placing, packing the objects etc.,[10-12]. PID logic controllers have some advantages compared to other controllers such as the simplicity of control, and ability to design control system without the need for accurate and precise mathematical model of a complex and non-linear system [13-14]. The DC motors are widely used in the variable position applications due to the ease of position control. In closed-loop system, the speed can be maintained constant by adjusting the motor terminal voltage. Smaller DC motors operate at 172

191 lower voltages, which make them easier to interface with control electronics. DC motors are commonly used where precise speed or position control is necessary, as in computer disk drives or in video cassette recorders, the spindles within, CD-ROM drives, and mechanisms within office products such as fans, laser printers and photocopiers etc. The detailed discussion of DC motors is presented in many books [15, 16]. The main advantage of the DC motor is low maintenance, high efficiency. Since DC motors are used especially in speed maintaining applications, speed is one of the important process parameter that is to be monitored and controlled in industry. A brushless DC motor basically consists of a shaft, a rotor assembly equipped with one or more permanent magnets arranged on the shaft, and a stator assembly which incorporates a stator component and phase windings. Rotating magnetic fields are formed by the currents applied to the coils. The rotor is formed of at least one permanent magnet surrounded by the stator, wherein the rotor rotates within the stator. Two bearings are mounted at an axial distance to each other on the shaft to support the rotor assembly and stator assembly relative to each other. To achieve electronic commutation, brushless DC motor designs usually include an electronic controller for controlling the excitation of the stator windings [17]. 5.2 Principle The principle and block diagram of the ARM processor based DC motor speed control system for solar tracking application is illustrated in Figure The block diagram consists of an ARM processor (LM3S9B96) controller, DC motor, solar panel and personal computer. 173

192 It has LM3S9B96 development board with built in ADC and RS232 features and a 6V 300mA solar panel fixed to the DC motor rotor. Communication between controller and the PC is established using RS232 serial communication to transmit the signal voltage produced by the panel. The DC motor control signals are connected to the controller PWM signal pins and the output of the panel connected to a load that would dissipate 3W to match the panel s rating. 3W at 6V corresponds to a current of 0.75A, so by Ohm s law; a load resistance was calculated as being 15Ω. A 15Ω resistor was the closest value found and was connected to the panel. The tracking device still requires power, but a 6V battery that is connected in a charging arrangement with the solar panel supplies it. The voltage across and current through the load was monitored using ADC channels of the controller, and was recorded every half an hour on a clear day into a data base. The readings were taken on a span of days that possessed similar conditions including no cloud cover. The output voltage of the ADC is calculated by using the following formula V out = (V in /V ref )*2 n V out is output obtained from the ADC V in input voltage given to the ADC V ref is the reference voltage given to the ADC n is number of bit s of resolution (8-bit,10-bit,12-bit) 174

193 5.3 Hardware Features The complete hardware of the system including LM3S9B96 development board card, driver circuit, DC motor, solar panel and personal computer are designed and fabricated indigenously in the present study. The following sections describe the hardware features. The system architecture of the proposed solar tracking system using ARM Processor LM3S9B96 is shown in Figure 5.1. The positional direction of the sun with respect to time has been measured and implemented as an algorithm in the controller. Then, the controller in the chip delivers an output, the corresponding PWM signals, to drive the stepping motors. Thus, the directions of the single dimensional solar platform can be tuned to achieve optimal energy, respectively. There are two modes in the controller as given below (1) Balancing mode: To set the initial position of the solar platform, we use switches are used for balancing position. The goal is to set boundary problems around for preventing too large elevating angles, which may make the solar panels crash the mechanism platform, and thus damage the motors and the platform. (2) Automatic mode: In this mode the controller continuously reads the Real Time Clock (RTC) and compares with the tabular values stored, if it matches with those values the corresponding positional values will be sent to the PWM generator which will make the motor to rotate solar panel towards sun shine. 175

194 176

195 By tuning the two-dimensional solar platform, the optimal efficiency of generating power will be achieved [10,18]. The present system relies on predefined motion mechanism according to time instead of fixed mechanism. The Real Time Clock (RTC) is the reference for system which sends signals to the control system. The heart/backbone of the control system is a microcontroller which determines motor direction and angle to adjust the system in such a way that the sun light falls orthogonally on the panel Solar Panel Module Despite the unlimited solar energy, harvesting it is a challenge mainly because of the inefficiency of the panels. Present research shows that different types of methodology have been proposed to improve the efficiency of solar panels [19 23]. Most of the panel installations that are done are all fixed arrays. As the day passes, the sun moves away from the facing position of the panel and thus the power output of the panel decreases. The best way to overcome this problem is to make a moveable solar panel using sun tracking mechanism. This tracking system will improve the efficiency for photovoltaic cell applications. A solar tracker is a device for orienting solar photovoltaic panel towards the sun direction. The sun s position in the sky varies with respect to time of day as the sun moves across the sky. Solar powered equipment works best when pointed towards the sun direction, so the solar tracker can increase the efficiency of such system over any fixed position systems. 177

196 In the present study, 6V 2Watt solar panel is fixed to the DC motor rotor, the output terminals (+ve and ve voltage) of the solar panel are connected to the ADC input lines of the microcontroller through the resistor to protect the controller circuit. LM35 temperature sensor is also placed with solar panel to measure the current temperature whose output line is connected to the ADC input of the controller. The PWM line of the controller is connected to the DC motor to control the position of the panel. Controller will generate the PWM signal according to the sun direction with respect to time which will make panel to face towards sun direction to obtain maximum intensity of sun light. The output voltage produced by the solar panel is measured with ADC and sent to the remote system to log the record to test the system efficiency comparatively with fixed panels. The panel output voltage is recorded with time and temperature to verify the relation with maximum power generation at high temperatures ARM Processor (LM3S9B96) Board: ARM processor is the main backbone of the control system in the present study. This determines the real time with RTC, generates PWM signal to control actuators and A/D conversion operation to convert analog signals in to digital form for processing. The architecture of ARM processor is discussed in chapter 3. It plays major role in data acquisition, measurement, display, record and generates control action to the process under investigation. The processor is used to measure voltages and control the DC motor position, PWM, A/D and RTC. 178

197 In the present study, UART, PWM, ADC and RTC features of the controller are enabled by setting the PIN SELECT register. The present time is accessed from RTC through I 2 C channel, obtained time values will be compared with relational table values to evaluate the position of the sun and produce PWM signal for controlling the position of the DC motor. The voltage generated by the solar panel and temperature are converted with 12 bit A/D converter to provide digital equivalent of the voltage, After that the samples are sent to personal computer (PC) with the help of one built-in feature of LM3S9B96 microcontroller. This is facilitated by the internal channel that can be configured for communication with other serial device (PC) through UART communication (RS-232 communication) to observe solar panel power performance. The serial data transmission has become so important to the overall computing strategy in industrial and commercial applications. In computer, the sample values are read by using serial communication with the help of visual basic (VB). The samples are stored in MS-Access data base file, by using file management library functions in VB language in PC. The solar panel output is observed (investigated) on Personal Computer in the form of graphical representation with the help of VB6 software. The performance of the solar tracking system is recorded for a day. Pulse Width Modulation (PWM) LM3S9B96 controller is having PWM as an internal feature, which will be enabled by setting the PIN SELECT register, the pulse width is controlled by setting the on-off time. Pulse-width modulation (PWM), or pulse-duration modulation (PDM), is a commonly used technique for controlling power to 179

198 internal electrical devices, made practical by modern electronic power switches. The Position of DC motor depends on the current through its armature coil. With coil motor the position can be changed by either varying the armature current or the field current although it is the armature current that is varied mostly. Thus, position control can be achieved by controlling the voltage applied to the armature [25-26]. The details of PWM are discussed in chapter 3. In the present study, the control signals for DC motor position control are emanated from the table designed to provide the relation between time and sun direction. In this study pulse width modulation (PWM) technique is used. This basically involves taking a constant DC supply voltage and chopping it so that the average value is varied. Hence PWM is used to control the average voltage applied to the armature. PWM wave is an analog signal that switches between two predefined limits. The switching interval of the PWM, controlled by the reference signal (D/A convert output), determines the average power delivered to the motor trough actuator. By varying the duty cycle of the PWM, the position of the motor is controlled. Analog to Digital Converter (ADC) ADC is an internal feature, which uses sampling to convert a continuous quantity to a discrete time representation in digital form. Basic clocking for the A/D converters is provided by the APB clock. A programmable divider is included in each converter, to scale this clock to the 4.5 MHz (max) clock needed by the successive approximation process. A fully accurate conversion requires eleven of 180

199 these clocks. An ADC may also provide an isolated measurement such as an electronic device that converts an input analog voltage or current to a digital number proportional to the magnitude of the voltage or current[27]. The ADC used in the present study has the following features: 10 bit successive approximation analog to digital converter Input multiplexing among 6 or 8 pins (ADC0 and ADC1) Power-down mode Measurement ranges 0 V to VREF (typically 3 V; not to exceed VDDA voltage level). 10 bit conversion time 2.44 µs Burst conversion mode for single or multiple inputs Optional conversion on transition on input pin or Timer Match signal Global Start command for both converters (LPC2144/6/8 only) In the present study, the panel output voltage and temperature obtained are analog signals, which cannot be processed with controller before processing as it has to be converted in to digital form. For that these signals are given to the ADC inputs of the controller which will convert the analog input signals in to equality digital signals. The converted digital signals are sent to the remote computer to store in database for future study. Real Time Clock (RTC) The Real Time Clock (RTC) is a set of counters for measuring time when system power is ON, and optionally when it is OFF. It uses little power in Power-down mode. On the LPC214x, the RTC can be clocked by a separate KHz 181

200 oscillator or by a programmable pre-scale divider based on the APB clock. Also, the RTC is powered by its own power supply pin, VBAT, which can be connected to a battery or to the same 3.3V supply used by the rest of the device [24]. The RTC used in the present study has the following features: Measures the passage of time to maintain a calendar and clock Ultra Low Power design to support battery powered systems Provides Seconds, Minutes, Hours, Day of Month, Month, Year, Day of Week, and Day of Year Dedicated 32 khz oscillator or programmable pre-scalar from APB clock Dedicated power supply pin can be connected to a battery or to the main 3.3V In the present study, the position of the sun is found out from the relational table designed with respect to time and position of the sun. To evaluate the position of the sun from the table, time has to be obtained and for that RTC is enabled and read time continuously from RTC registers and extracts the position from the table and produce appropriate PWM signal to rotate the motor Relay Circuit A relay is an electromagnetic switch. In other words it is activated when a current is applied to it. Normally a relay is used as switch in circuits as shown in the Figure 5.2. There are different types of relays and they operate at different voltages. When a relay circuit is build, one need to consider the voltage that will trigger it [28 29]. 182

201 In the present study, the output of the PWM signal is connected to the DC motor through the relay circuit to control the motor. The PWM signal is passed via relay circuit to protect the controller board from the back EMF produced by the DC motor while switching between forward and reverse directions. The relay circuit will also increase the strength of the controller signal which cannot drive the motor, so that the motor can be energized. Preset Resistor Motor Transistors Figure 5.2 Relay Circuit diagram Personal Computer: Here Personal computer is the most important and integral part of the entire control system as it plays a vital role in data acquisition, display, record, and generate graphs. The predominant interfaces between personal computer and microcontroller are LPT, RS232 and ethernet. In this context RS232 communication has been 183

202 implemented to transfer data from controller to PC to display and store in database. Hence, a personal computer with the following future is employed in the present study for graphical user interface design and recording data for feature analysis. Pentium IV Processor - 2.0GHz frequency 512MB RAM 20 GB HDD One RS232 compatible Serial Port Besides above-mentioned hardware configuration computer is loaded with windows XP operating system and Visual Basic6.0 version. For providing flexibility and storability GUI software has been developed in VB6, which will provide a graphical visualization for the user to monitor the status of the signal and store the data for future study. In the present study panel output voltage and temperature are measured and stored in to the data base with date and time. The output of user interface details is shown in Figure

203 (a) Fixed panel Solar System (b) Proposed Solar tracking System Figure 5.3 Output of user interface details [ (a). Fixed panel, (b). proposed solar tracking system] 185

204 5.4 Experimental Implementation The experimental implementation of the Real Time Clock based solar tracking system is accomplished by developing the necessary software for the hardware. The controlling software is implemented using embedded C programming language in Keil4 cross compiler IDE. The following discussion epitomizes the design methodology of RTC, PWM and ADC logic functionalities for the proposed system. The controller provides the reasonable tracking of the motor speed to pre-specified reference speed. RTC and PWM functionalities have been implemented to control the position of the solar panel towards sun direction, time and sun direction relation algorithm table has been implemented to generate the PWM signal to control the position of the panel The solar panel temperature sensor and DC motor are arranged properly and it is interfaced with microcontroller. The micro controller acquires the voltage output from the panel and temperature through built-in 10-bit A/D converter and substitutes in a well-calibrated equation to evaluate the actual speed of the motor. The complete circuit schematic of the application of DC motor speed control for solar tracking system is shown in Figure

205 187 Figure 5.4 : The compete circuit schematic of the application of DC motor speed control for solar tracking system

206 5.5 Software Features The following section describes the software development for DC motor position control system and PWM implementation in embedded C programming language in detail. The necessary algorithm, flowchart and C language programs are presented and thoroughly discussed. The software developed for generating PWM signal to control the DC motor position with respect to time and ADC functionality code to convert analog voltage produced by the panel. This system also provides the graphical user interface (GUI) to display and plot the graphs of the voltage and temperature signals of the panel. The complete software is divided into two major parts viz., a) measurement and b) control. Besides the two major tasks of measurement and control the software first declares and initializes all program variables, constants and functions. It also initializes system hardware i.e.., RTC, ADC, UART and PWM. The software then enters the measurement and control programs. In measurement, the software acquires the voltage produced by the panel and temperature of the panel. On the other hand, the control software will continuously monitor the time and compares with prerecorded values. Once the values are matches it will produce PWM signals to control the position of the motor, through which panel position will be controlled. Flow chart The flowchart is so drawn that it is self-explanatory and gives the complete idea of how computer sequentially does the different steps involved in measurement and 188

207 control. The flow chart of motor position control system for solar tracking application is shown in Figure 5.5. RTC based solar tracker has been implemented for controlling the position of the panel by controlling the DC motor. The panel position is controlled and the performance of the solar tracking panel is tested with fixed panel. The design and development of RTC based solar tracker has been implemented by controlling the position of the DC motor, for checking the performance of the solar panel with tracker and with fixed setup. Flow-chart for system software development is shown in Figure

208 Start Initialize RTC, PWM and ADC Read time and date from RTC Compare time with relational table If match with exist values No Yes Generate PWM signal to rotate the motor Read panel O/P voltage & temperature Send voltage and temp to remote dev through RS232 Figure 5.5 Flow chart of ARM processor based solar tracking system. 190

209 Start Enable serial RS232 communication port Read Data from serial Port Store to DBLDR DB Enable serial RS232 communication port Figure 5.6 Flow-chart for system software development. 5.6 Results and Discussion: The present study applies for solar cell panel which is connected to motor and is controlled through the microcontroller. The output voltage of the panel will be read through the ADC channel of the controller and the converted digital voltage values are sent to the remote device through the RS232 communication channel. At the receiver side the personal computer will receive the signals sent by the controller and stores the readings in the data base. Initially the panel output voltage readings have been measured for a day by fixing the panel in a fixed 191

210 direction, again tested by making panel rotatable according to the sun tracking using RTC. These values are recorded for a day and the results have been compared with each other. The tracking system gives 40% efficiency than a fixed system. Solar energy tracking for fixed panel is shown in Figure 5.7 A solar tracker is designed employing the new principle of using Real Time Clock (RTC), providing a variable indication of their relative angle to the sun by comparing with pre-defined measured readings. By using this method, the solar tracker was successful in maintaining a solar array at a sufficiently perpendicular angle to the sun. The power increase gained over a fixed horizontal array was in excess of 40%. Solar energy tracking for proposed panel is shown in Figure 5.8 and Comparison of Fixed and Proposed panel of ARM based solar tracking system is shown in Figure 5.9. And also observed the stability of output for week duration and same is shown in Figure

211 Figure5.7 Solar energy tracking for fixed panel Figure 5.8 Solar energy tracking for proposed panel 193

212 Proposed panel Fixed panel Figure 5.9 Comparison of Fixed and Proposed panel of solar tracking system Proposed panel Fixed panel Figure 5.10 Solar energy tracking for one week 194

213 The present results are compared with the work of J.Rizk, and Y. Chaiko., Y. J. Huang, T. C. Kuo et al., group [40-41] where they designed and implemented Solar tracking system using LDR. They carried out the experimental analysis for solar tracker to achieve maximum power. On comparing the obtained results with them, the proposed solution for a solar tracking system offers several advantages concerning the movement of the photo voltaic panel: With the usage of LM3S9B96 controller reduced hardware complexity circuit for controlling DC motor speed. The microcontroller contains all peripherals as built-in facility. An optimum cost/performance ratio, which is achieved via the simplicity of the adopted mechanical solution and the flexibility of the intelligent command strategy; A minimum of energy consumption, due to the fact that the panel movement is carried out only in justified cases. A maximization of output energy produced by the PV panel, through an optimal positioning executed only for sufficient values of light signal intensity; The centralized monitoring and diagnosis of the system operation. Photographs of ARM Processor Kit board and complete view of experimental setup and working model are shown in Figure

214 (a) (b) Figure 5.11 Photographs of (a) ARM Processor Kit board and (b) Complete view of experimental setup and working model 196