Introduction to Automation System

Transcription

1 Introduction to Automation System Chapter Automation Automation or automatic control is the use of various control systems for operating equipment such as machinery, processes in factories, boilers and heat treating ovens, switching on telephone networks, steering and stabilization of ships, aircraft and other applications with minimal or reduced human intervention. Some processes have been completely automated. The biggest benefit of automation is that it saves labour; however, it also used to save energy and materials and to improve quality, accuracy and precision. The term automation inspired by the earlier word automatic (coming from automaton), was not widely used before 1947, when General Motors established the automation department. It was during this time that industry was rapidly adopting feedback controllers which were introduced in the 1930s and Automation has been achieved by various means including mechanical, hydraulic, pneumatic, electrical, electronic devices and computers, usually in combination. Complicated systems, such as modern factories, airplanes and ships typically use all these combined techniques. In practice, the design of the parameters tuning drive involves a complex process such as modeling, control scheme selection, simulation etc. An expert knowledge of the system is required for tuning the controller parameters of system to get the optimal performance. Conventional PID controller algorithm is simple, stable, easy adjustment and high reliability, Conventional control system used in conventional PID control.most of industrial processes with different degrees of nonlinear, parameter variability and uncertainty of mathematical model of the system. Tuning PID control parameters is very difficult, poor robustness; therefore, it's difficult to achieve the optimal state under field conditions in the actual production. FPID control method is a better method of controlling, to the complex and unclear model systems has it can give simple and effective control where Play fuzzy control robustness, good dynamic response, rising time, overstrike characteristics. 1

2 The main advantage of automation is increased throughput or productivity and consistency of output. Improved quality or increased predictability of quality and robustness of processes or product and also reduced direct human labour costs and expenses. The following methods are often employed to improve productivity, quality, and robustness. Install automation in operations to reduce cycle time. Install automation where a high degree of accuracy is required. Replacing human operators in tasks that involve hard physical or monotonous work. Replacing humans in tasks done in dangerous environments (i.e. fire, space, volcanoes, nuclear facilities, underwater, etc.) Performing tasks that are beyond human capabilities of size, weight, speed, endurance, etc. Automation may improve in economy of enterprises, society or most of humanity. For example, when an enterprise invests in automation, technology recovers its investment; or when a state or country increases its income due to automation like Germany or Japan in the 20th Century. Reduces operation time and work handling time significantly. Frees up workers to take on other roles. Provides higher level jobs in the development, deployment, maintenance and running of the automated processes. The main disadvantages of automation are: Security Threats/Vulnerability: An automated system may have a limited level of intelligence, and is therefore more susceptible to committee errors outside of its immediate scope of knowledge. Unpredictable/excessive development costs: The research and development cost of automating a process may exceed the cost saved by the automation itself. High initial cost: The automation of a new product or plant typically requires a very large initial investment in comparison with the unit cost of 2

3 the product, although the cost of automation may be spread among many products and over time. In manufacturing, the purpose of automation has shifted to issues broader than productivity, cost, and time. 1.2 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 [1]. The process has a property called self-regulation. A self- regulating 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 [2, 3]. 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 and Programmed control such as the temperature control of heating furnaces are 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 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 3

4 most process control systems include servomechanism as an integral part [4].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 parameters. 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. 1.3 Proportional plus Integral Plus Derivative Controller (PID) As Proportional plus Derivative improves transient and Proportional plus Integral improves steady state, combination of the two may be used to improve overall time response of the system.while PD control 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-integral-differential 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 [5, 6].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 [7].As the PID controller is composed of three components, it produces an output signal consisting of three termone is proportional to the error signal e (t), another is proportional to integral of error signal e (t) and the third one is proportional to derivative of the error signal e(t) [8-10].The equation of the PID controller given as 4

5 The block diagram representation of the PID controller as shown in Figure 1.1 and block diagram of the PID based control system is depicted in Figure 1.2. Figure 1.1: Block diagram representation of the PID controller Figure 1.2: 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 At any instant of time, the current value of the PID output Vn is calculated basedon 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 5

6 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 the majority in industrial processes. The first reason is of its simple structure and the well-known Ziegler- Nichols tuning algorithms have been developed [11]. The second reason is of its controlled processes in industrial plant can be controlled through the PID controller [12, 13]. 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 by using the different prediction step. Figure 1.3: Block diagram of software and hardware of PID control system It is fact that PID controller is very widely used in industry and 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 [17]. 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. 6

7 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 [18,19]. 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. The proportional controller stabilizes the gain but produces a steady state error. The 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 [20-25] 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 Where P contrib, I contrib, and D contrib are the feedback contributions from the PID controller as represented below 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. 7

8 Kp: Proportional Gain Larger K p typically means faster response since the larger the error, larger is the feedback to compensate. K i : Integral Time Small K 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. K d : Derivative Time Larger K d decreases overshoot, but slows down transient response [26-30]. PID is a feedback based controller which gets the error value and calculates the output based on the characteristics of the error. It is very widely used in plants as it is simple and gives good result. Figure 1.4: Block diagram of the PID based process control system PID is used in a closed loop.it ha s three elements P,I,D. Every parameter has gain by which we control the contribution. PID Alogorithm: where P out : Proportional term of output K p : Proportional gain, a tuning parameter K i : Integral gain, a tuning parameter K d : Derivative gain, a tuning parameter e: Error = SP PV and t: Time or instantaneous time (the present) 8

9 Proportional term The proportional term makes a change in the output that is proportional to the current error value. The proportional response can be adjusted by multiplying the error by a constant K p, called the proportional gain. The proportional term is given by: Derivative term The derivative of the process error is calculated by determining the slope of the error over time and multiplying this rate of change by the derivative gain K d. The magnitud e of the contribution of the derivative term to the overall control action is termed the der ivative gain. The derivative term is given by: Integral term The contribution from the integr al term is proportional to both the magnitude of the error and the duration of the error. The int egral in a PID controller is the sum of the instantaneous error over time and gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied by the integral gain (K i ) and added to the controller output. The integral term is given by: 1.4 Tuning methods of PID Performance of PID depends on the gain parameters. so we need to adjust them.different methods are used as I) open loop method and ii) close loop method i) Open Loop Method In this method a step to the process and get the response like as shown in the graph and get the deadtime,reaction rate and process gain then by putting the controller in manual mode, wait until the process value (Y) is stable and not changing Step output of the PID controller - The step must be big enough to see a significant change in the process value. A rule of thumb is the signal to noise ratio should be greater than 5. Repeat making the step in 9

10 the opposite direction.k = the process gain=change in process value /change in manipulated value. ii) close loop method Ziegler Nichols 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 [14]. 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 [15] are often applied for parameter identification to overcome the above problems. A further modification to the ZN methods can give a substantially improved system performance [16]. Another tuning method is formally known as the Ziegler Nichols method, by John G. Ziegler and Nathaniel B. Nichols in the As in the method above, the K i and K d gains are first set to zero. The P gain is increased until it reaches the ultimate gain, K u, 10

11 at which the output of the loop starts to oscillate. K u and the oscillation period P u are used to set the gains as shown in Table 1.2. Table 1.2 comparison of P,PI and PID Cohen-Coon Method (Open-loop Test) Step 1: Perform a step test to obtain the parameters of a FOPTD (first order plus time delay) model Make sure the process is at an initial steady state Introduce a step change in the manipulated variable Wait until the process settles at a new steady state Step 2: Calculate process parameters. Step 3: Using the process parameters, use the prescribed values given by Cohen and Coon Figure 1.5: Step Test for Cohen-Coon Tuning Tyreus-Luyben Method (Closed-loop P-Control test) Step 1-4: Same as steps 1 to 4 in Ziegler-Nichols method as presented above Step 5: Evaluate control parameters as prescribed by Tyreus and Luyben Auto tune Method (Closed-loop On-Off test) Step 1: Let process settle to a steady state Step 2: Move the set point to the current steady state Step 3: Implement an on-off (relay) 11

12 Step 4: Let the process settle to a sustained periodic oscillation controllerr Step 5: Evaluate ultimate gain using auto tune formulas Step 6: Use either Ziegler-Nichols or Tyreus-Luyben prescribed tunings Figure 1.6: Step Test for auto tune method The main advantage of the closed-loop tuning method is that it considers the dynamics of all system components and therefore gives accurate results at the load where the test is performed. Another advantage is that the readings of Ku and Pu are easy to read and the period of oscillation can be accurately read even if the measurement is noisy.the disadvantages of the closed-loop tuning method are that when tuning unknown processes, the amplitudes of undampened oscillations can become excessive (unsafe) and the test can take a long time to perform and One can see that when tuning a slow process (period of oscillation of over an hour),it can take a long time before a state of sustained, undampened oscillation is achieved through this trial-and-error technique. For these reasons, other tuning techniques have also been developed and some of them are described below. First, it is essentially trial- the ultimate and-error methods, since several values of gain must be tested beforee gain. Second by while one loop is being tested in this manner, its outpu may affect several other loops, thus possibly upsetting an entire unit Manual tuning If the system must remain online, one tuning method is to first set and values to zero. Increase the until the output of the loop oscillates, then the should be set to approximately half of that value for a "quarter amplitude decay" type response. Then increase until any offset is corrected in sufficient time for the process. However, too much will cause instability.finally, increase, if required, until the loop is acceptably quick to reach its reference after a load disturbance. However, 12

13 too much will cause excessive response and overshoot. A fast PID loop tuning usually overshoots slightly to reach the set point more quickly; however, some systems cannot accept overshoot, in which case an over-damped closed-loop system is required, which will equire a setting significantly less than half that of the setting that was causing oscillation. Overview of methods There are several methods for tuning a PID loop. The most effective methods that are generally involve the development of some form of process model, and then choosing P, I, and D based on the dynamic model parameters. Manual tuning methods can be relatively time consuming, particularly for systems with long loop times. The choice of method will depend largely on whether or not the loop can be taken "offline" for tuning, and on the response time of the system. If the system can be taken offline, the best tuning method often involves subjecting the system to a step change in input, measuring the output as a function of time, and using this response to determine the control parameters. Table 1.3 Effects of varying PID parameters (K p,k i,k d ) on the step response of a system Figure 1.7: Oscillation graph 13

14 Table 1.4 Comparison of tuning techniques of PID Typical steps for designing a PID controller Determine what characteristics of the system need to be improved. Use K P to decrease the rise time. Use K D to reduce the overshoot and settling time. Use K I to eliminate the steady-state error Damping oscillation Damping is an influence within or upon an oscillatory system that has the effect of reducing, restricting or preventing its oscillations. In physical systems, damping is produced by processes that dissipate the energy stored in the oscillation. The damping of a system can be described as being one of the following. 14

15 Overdamped oscillation The system returns (exponentially decays) to equilibrium without oscillating. Critically damped oscillation The system returns to equilibrium as quickly as possible without oscillating. Underdamped oscillation The system oscillates (at reduced frequency compared to the undamped case) with the amplitude gradually decreasing to zero. Undamped oscillation The system oscillates at its natural resonant frequency (ω o ). Figure 1.8: Different Damping Oscillations Figure 1.9: Damping Oscillations of the System graph 15

16 Figure 1.10: Oscillations for K P Figure 1.11: Oscillations for K i Figure 1.12: Oscillations for varying K d 16

17 1.5 Fuzzy logic Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 [31]. By contrast, in Boolean logic, the truth values of variables may only be 0 or 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when linguistic variables are used, these degrees may be managed by specific functions. The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by Lotfi A. Zadeh. Fuzzy logic has been applied to many fields, from control theory to artificial intelligence. Fuzzy logic had however been studied since the 1920s, as infinite-valued logic notably by Łukasiewicz and Tarski. Figure 1.13: Block diagram of Fuzzy Figure 1.14: Benefits of Fuzzy 17

18 Methods of Fuzzification AI (adaptive integration) BADD (basic fuzzification distributions) BOA (bisector of area) CDD (constraint decision fuzzification) COA (center of area) COG (center of gravity) ECOA (extended center of area) EQM (extended quality method) FCD (fuzzy clustering fuzzification) FM (fuzzy mean) FOM (first of maximum) GLSD (generalized level set fuzzification) ICOG (indexed center of gravity) IV (influence value) LOM (last of maximum) MeOM (mean of maxima) MOM (middle of maximum) QM (quality method) RCOM (random choice of maximum) SLIDE (semi-linear fuzzification) WFM (weighted fuzzy mean) Defuzzification The procedure of producing a output variable in fuzzy logic given rule set of fuzzy and respective membership values can be described as term Defuzzification. It requires basically fuzzy control matrix arrangement.it contain large number of conditions which will convert the linguistic variables of a final result taken from the fuzzy logic [32]. To understand this, ruleset of the fuzzy logic is to be determine how much PWM drive can be applied to the driver of output.use the Mean of Maximum (MoM) defuzzification method for pattern recognition applications. This defuzzification method calculates the most plausible result. Rather than averaging the 18

19 degrees of membership of the output linguistic terms, the MoM defuzzification method selects the typical value of the most valid output linguistic term.the following image illustrates the MoM defuzzification method. Figure 1.15: Mean of Maxima method Linguistic variables While variables in mathematics usually take numerical values, in fuzzy logic applications, the non-numeric are often used to facilitate the expression of rules and facts[33]. A linguistic variable such as age may have a value such as young or its antonym old. However, the great utility of linguistic variables that can be modified via linguistic hedges applied to primary terms. These linguistic hedges can be associated with certain functions. 1.6 Fuzzy with PID control Most industrial processes with different degrees of nonlinear, parameter variability and uncertainty of mathematical model of the system.tuning PID control parameters is very difficult, poor robustness, therefore, it's difficult to achieve the optimal state under field conditions in the actual production [34]. FPID control method is a better method of controlling, to the complex and unclear model systems, it can give simple and effective control, Play fuzzy control robustness, good dynamic response, rising time, overstrike characteristics. The control architecture is implemented with the FPID logic to control the process parameters, due to its excellent control characteristics. The Fuzzy and PID controllers are successfully applied to many practical process control applications involving the physical parameters like liquid temperature, liquid level, liquid flow, pressure, humidity, rotational speed of motor etc. The methodology encompasses the design of 19

20 FPID controllers for process parameters controlling with less rising time, settling time as well as steady state error. Fuzzy with PID control is the best algorithm compared to PID and Fuzzy control algorithms. Result of it is reducing the rise time as well as settling time and steady state error. It improves robustness, good dynamic response, rising time, overstrike characteristics. The advantage of the designed model over the available auto tune PID using tuning methods is that, it does not requires any mathematical modeling of the process. While conventional PID controllers are sensitive to variations in the system parameters, fuzzy controllers do not need precise information about the system variables in order to be effective. However, PID controllers are better able to control and minimize the steady state error of the system. Hence, a FPID system was developed to utilize the advantages of both PID controller and fuzzy controller. Figure 1.16: Block diagram of FPID Figure 1.17: Comparison of Fuzzy and PID 20

21 1.7 Review of Earlier Literature Jen-Yang Chen[35] 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 [36] 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 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.,[37] reported that optimal PID control signal similar to the Modelbased 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.,[38] 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., [39] reported the benchmark construction with application to PID controller design and implementation. A benchmark is constructed from a real system, 21

22 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 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. Yus of R.et al.,[40] 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 [41] 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. A. Visioli et al.,[42] presented fuzzy logic, for the tuning of proportional-integralderivative (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.,[43] 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. Nusret Tan et al.,[44] 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 22

23 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. Mohamed I. Abu El-Sebah et al., [45] 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 results of the simulation indicate that the PID controller is effective and powerful for PM drive systems. A. Rubaai, M. J. Castro-Sitiriche et al., [46] 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., [47] presented an adaptive single neuron based PID speed controller to substitute the traditional PID controller.the parameters of PID controller are selected by immune algorithm to obtain the required response. Saeed Tavakoli et al., [48] 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 system shows a robust performance in the face of model parameters uncertainty. Panagopoulos, H, Astrom, K.J et al.,[49] 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., [50] presented a function-based evaluation approach for a systematic study of fuzzy proportional-integral-derivative 23

24 (PID)-like controllers. This approach is applied for deriving process-independent 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 gain-scheduling controllers. Gogoi, Manoj et al.,[51] 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. The results were satisfactory and robust stability was achieved for the perturbed plants. Huang, Y.Yasunobu et al.,[52] 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.,[53] 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 multipleinput/multiple-output plants to attenuate unknown disturbances. El-Gammal. A.A.A. El-Samahy, A.A. et al.,[54] presented the application of a new particle swarm optimization technique for adjusting the gains of a PID speedcontroller 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. Since the optimal PID controller 24

25 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. S. M. Giri Rajkumar et al.,[55] 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. C. C. Hang, K. J. Astrom et al.,[56] 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. Asim Ali Khan, Nushkam Rapal et al.,[57] 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. Subrata Chattopadhyay et al., [58] 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 25

26 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.,[59]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. W. K, Hang C. C, Cao L. S, et al., [60] presented simple formulae to tune/design the PI and PID controllers to meet user-specified gain 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 [61] 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., [62] 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 using genetic algorithm technique. 26

27 Maruthai Suresh, et al., [63] 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. 1.8 Motivation for present work In the present research work, literature survey was carried out on automation system for certain industrial process parameters, Arduino mega260microcontroller family, and application of PID logic, Fuzzy logic and Fuzzy with PID controllers in the field of process control instrumentation. Most of the research was reported on the application of PID logic controller for controlling the process parameters. Manual tuning method based proportional-integral-derivative (PID) controller and Mean of maxima method of Fuzzy logic control is proposed and integrated into a digital feedback control system. 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. Further, the designing of hardware is reduced the and real time implementation of PID logic, Fuzzy logic and Fuzzy with PID controllers for process control is very rare. Even if so, it is not found in any research paper or report that the development of Arduino Mega2560 microcontroller based complete hardware (including necessary signal conditioning and control circuitry) and development of software to realize PID, Fuzzy logic and Fuzzy with PID controllers 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 development of both the hardware and software aspects of Arduino Mega2560 microcontroller PID logic, Fuzzy logic and Fuzzy with PID controllers for the process parameter such as process parameter measurement and controlling. The present study emphasizes the complete development of the microcontroller based control systems. 27

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