S. Eswar Prasad, Adjunct Professor, Department of Mechanical & Industrial Engineering, Chairman, Piemades Inc, Piemades, Inc.

Transcription

1 Lecture 1: Introduction to Smart Materials and Systems Lecture 2: Sensor technologies for smart systems and their evaluation criteria. Lecture 3: Actuator technologies for smart systems and their evaluation criteria. Lecture 4: Piezoelectric Materials and their Applications. Lecture 5: Control System Technologies. Lecture 6: Smart System Applications. S. Eswar Prasad, Adjunct Professor, Department of Mechanical & Industrial Engineering, Chairman, Piemades Inc, Piemades, Inc. 1

2 Control Technologies for Smart Systems S. Eswar Prasad, Adjunct Professor, Department of Mechanical & Industrial Engineering, Chairman, Piemades Inc, Piemades, Inc. 2

3 Control Technologies for Smart Systems Control Systems Overview Open loop and closed loop systems Control System Characteristics Steady State, Transient Response and Stability Controller Operation Proportional, Compensated Digital Control Systems Control algorithms, implementation, hardware Control System Design 3 3

4 Control Systems Overview Deals with influencing the behaviour of dynamic systems Interdisciplinary field, which originated in engineering and mathematics, and evolved into use by the social sciences, like psychology, sociology, criminology and in financial systems. Control systems have four basic functions; Measure, Compare, Compute, and Correct. These four functions are completed by three elements; Sensors, Actuators, Control System. In a smart system, these three elements are typically contained in one unit. 4

5 Control Systems Overview Definition of a Smart System 5

6 Control Systems Overview Historical Feedback control (Bode,1945) Theory of Stochastic Processes (Weiner, 1930) Root Locus Theory (Evans, 1948) Modern Control (1950s, Kalman, Bellman, Pontryagin) Root locus theory remains an important technique today. Suitable for design and stability analysis. Root locus analysis is a graphical method for examining how the roots of a system change with variation of a certain system parameter, commonly the gain of a feedback system. This is a technique used in the field of control systems developed by Walter R. Evans. 6

7 Control Systems Overview Root locus approach Assumption The definition of the damping ratio and natural frequency presumes that the overall feedback system is well approximated by a second order system, that is, the system has a dominant pair of poles. Uses Determine the stability of the system Design for the damping ratio and natural frequency of a feedback system. Lag, lead, PI, PD and PID controllers can be designed approximately with this technique. 7

8 Control Systems Overview Example: cruise control of a car Cruise Control is a device designed to maintain vehicle speed at a constant desired or reference speed provided by the driver. The controller is the cruise control, the plant is the car, and the system is the car and the cruise control. The system output is the car's speed, and the control itself is the engine's throttle position which determines how much power the engine generates. 8

9 Control System Technology Method-1. Implement cruise control by simply locking the throttle position when the driver engages cruise control. However, if the cruise control is engaged on a stretch of flat road, then the car will travel slower going uphill and faster when going downhill. Method-2. Use the system output (the car's speed) to control the throttle position. As a result, the controller can compensate for changes acting on the car, like a change in the slope of the road. 9

10 Control Systems Overview Types of Control systems Open Loop Control Closed Loop Control 10

11 Open Loop System Reference Input Controller Actuating Signal Controlled Process (Plant) Controlled Variable (output) General block diagram of an open-loop system An open-loop controller is often used in simple processes because of its simplicity and low cost, especially in systems where feedback is not critical An open-loop controller, also called a non-feedback controller, is a type of controller that computes its input into a system using only the current state and its model of the system. 11

12 Open Loop System Reference Input Controller Actuating Signal Controlled Process (Plant) Controlled Variable (output) General block diagram of an open-loop system A characteristic of the open-loop controller is that it does not use feedback to determine if its output has achieved the desired goal of the input. This means that the system does not observe the output of the processes that it is controlling. It also may not compensate for disturbances in the system. 12

13 Open Loop System Reference Input Controller Actuating Signal Controlled Process (Plant) Controlled Variable (output) General block diagram of an open-loop system Typical examples: Washing Machine, for which the length of machine wash time is entirely dependent on the judgment and estimation of the human operator. Some Irrigation Sprinklers are programmed to turn on/off at set times. It does not measure soil moisture as a form of feedback. Even if rain is pouring down on the lawn, the sprinkler system would activate on schedule, wasting water. 13

14 Closed Loop System Reference Input Error Detector + - Error Signal Controller Actuating Signal Controlled Process (Plant) Controlled Variable (output) Feedback Signal Feedback Path Elements General block diagram of a closed-loop control system A closed-loop controller uses feedback to control states or outputs of a dynamical system. Its name comes from the information path in the system: Process inputs have an effect on the process outputs, which is measured with sensors and processed by the controller; the result (the control signal) is used as input to the process, closing the loop. 14

15 Closed Loop System Reference Input Error Detector + - Error Signal Controller Actuating Signal Controlled Process (Plant) Controlled Variable Feedback Signal Feedback Path Elements General block diagram of a closed-loop control system Closed-loop controllers have the following advantages over open-loop controllers: Reduce error (eliminating the error) Reduce sensitivity or Enhance robustness Disturbance rejection or elimination Improve dynamic performance or adjust the transient response (such as reduce time constant) rejection (such as unmeasured friction in a motor) unstable processes can be stabilized improved reference tracking performance 15

16 Closed Loop System Reference Input + - Error Error Signal Controller Actuating Signal Controlled Process (Plant) Controlled Variabl Feedback Signal Feedback Path Elements General block diagram of a closed-loop control system Examples of Closed Loop Systems The mouse on a computer A joystick on a video game An air conditioning system or a heating system in a house. The speed control (cruise control) on an automobile. 16

17 Closed Loop System Sensor The output of the system y(t) is fed back through a sensor measurement F to the reference value r(t). The controller C then takes the error e (difference) between the reference and the output to change the inputs u to the system under control P. This kind of controller is a closed-loop controller or feedback controller. This is called a single-input-single-output (SISO) control system; MIMO (i.e., Multi-Input-Multi-Output) systems, with more than one input/output, are common. In such cases variables are represented through vectors instead of simple scalar values. 17

18 Closed Loop System Sensor If we assume the controller C, the plant P, and the sensor F are linear and timeinvariant (i.e., elements of their transfer function C(s), P(s), and F(s) do not depend on time), the systems above can be analyzed using the Laplace transform on the variables. This gives the following relations: Solving for Y(s) in terms of R(s) gives: 18

19 Closed Loop System Sensor The expression is referred to as the closed-loop transfer function of the system. The numerator is the forward (open-loop) gain from r to y, and the denominator is one plus the gain in going around the feedback loop, the so-called loop gain. If, i.e., it has a large norm with each value of s, and if, then Y(s) is approximately equal to R(s) and the output closely tracks the reference input. 19

20 Selection of a Control System An open-loop system Trade-offs Simplicity and low cost Complexity and higher cost A closed-loop system 20

21 Elements of Control Systems Response Characteristics Steady State Response Transient Response Stability 21

22 Amplitude Elements of Control Systems Response Characteristics Steady State Response is defined as the output of the plant. Difference between final value and the desired value is known as the steady-state error. Overshoot Steady State Error Input Command Transient Response Steady State Response Time 22

23 Amplitude Elements of Control Systems Response Characteristics Transient Response is defined as the change undergone by plant from time input is applied to the time taken to reach steady state. The ideal situation is to reach the final state accurately and in as little time as possible. Four parameters define the transient response. Overshoot Steady State Error Input Command Transient Response Steady State Response Time 23

24 Amplitude Elements of Control Systems Response Characteristics Overshoot Steady State Error Input Command Transient Response Steady State Response Time Settling time, ts, is the time it takes output to settle within a specified boundary typically 2%). Rise time, tr, is the time it takes for the output to change from 10% to 90% of final value. Peak time, tp, is the time to reach the vicinity of set point, and usually the largest, peak. Overshoot, Mp, is the amount that the peak exceeds the steady state value at the peak time. generally expressed as a percentage of the final steady state value. 24

25 Elements of Control Systems Response Characteristics Stability is defined as the ability of a control system to achieve its goal without going into oscillation. The total response of a control system is a combination of the natural response, totally governed by the plant, and the forced response, typically governed by the controller. It is a mandatory requirement that a control system be stable. 25

26 Open loop Closed Loop Step response of a control system 26

27 Elements of Control Systems Response Characteristics Considering a second order system, we can derive expressions for the terms using the pole location parameters ζ and ωn. 27

28 Elements of Control Systems Response Characteristics Considering a second order system, we can derive expressions for the terms using the pole location parameters ζ and ωn. 28

29 Controller Operation Controller provides a means to allow the output of a system to track the input. Using frequency domain analysis methods, the transfer function can be expressed as, Ideally, Y(s)=1, and the output tracks input perfectly. Controllers are broadly classified into two types. Proportional Controllers Compensated Controllers 29

30 Proportional Controllers In the proportional control algorithm, the controller output is proportional to the error signal, which is the difference between the set point and the process variable. In other words, the output of a proportional controller is the multiplication product of the error signal and the proportional gain. This can be mathematically expressed as where Pout: Output of the proportional controller Kp: Proportional gain e(t): Instantaneous process error at time 't'. e(t) = SP PV SP: Set point PV: Process variable 30

31 Compensated Controllers Compensation is a technique used to change the root locus so that it passes through a desired pole position. This process involves the selective positioning of additional poles and zeros into the overall response of the system. Compensation can be used to improve both the steady state error and the transient response. Most controllers now are implemented digitally. 31

32 PID Controllers A proportional integral derivative controller (PID controller) is a generic control loop feedback mechanism (controller) widely used in industrial control systems a PID is the most commonly used feedback controller. A PID controller calculates an "error" value as the difference between a measured process variable and a desired set point. The controller attempts to minimize the error by adjusting the process control inputs. P u(t) + - e(t) I Plant/Process y(t) D 32

33 PID Controllers The PID controller calculation (algorithm) involves three separate constant parameters, the proportional, the integral and derivative values, denoted P, I, and D. These values can be interpreted in terms of time: P depends on the present error, I on the accumulation of past errors, and D is a prediction of future errors, based on current rate of change. The weighted sum of these three actions is used to adjust the process via a control element. In the absence of knowledge of the underlying process, a PID controller is the best controller. By tuning the three parameters in the PID controller algorithm, the controller can provide control action designed for specific process requirements. The the use of the PID algorithm for control does not guarantee optimal control of the system or system stability. CONTROL SYSTEMS, ROBOTICS, AND AUTOMATION Vol. II - PID Control - Araki M

34 PID Controllers In PID control the sum of its three correcting terms constitutes the manipulated variable (MV). The proportional, integral, and derivative terms are summed to calculate the output of the PID controller. Defining u(t) as the controller output, the final form of the PID algorithm is: where Kp : Proportional gain, a tuning parameter Ki : Integral gain, a tuning parameter Kd : Derivative gain, a tuning parameter e : Error = SP PV t : Time or instantaneous time (the present) 34

35 PID Controllers P u(t) + - e(t) I Plant/Process y(t) D Parameter Rise Time Overshoot Settling Time Steady-state Error Stability Kp Decrease Increase Small Change Decrease Degrade Ki Decrease Increase Increase Large decrease Degrade Kd Small Increase Small decrease Small decrease No effect in theory Improve if Kd is small Ang, K.H., Chong, G.C.Y., and Li, Y. (2005) PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology, 13 (4). pp Jinghua Zhong (2006). PID Controller Tuning: A Short Tutorial. 35

36 PID Controllers Open Loop step response (OL) Proportional Control (P) Proportional Derivative control (PD) Proportional Integral control (PI) Proportional-Integral-Derivative Control (PID) 36

37 PID Controllers - Limitations While PID controllers are applicable to many control problems, and often perform satisfactorily without any improvements or even tuning, they can perform poorly in some applications, and do not in general provide optimal control. The fundamental difficulty with PID control is that it is a feedback system, with constant parameters, and no direct knowledge of the process, and thus overall performance is reactive and a compromise. 37

38 PID Controllers - Limitations PID controllers, when used alone, can give poor performance when the PID loop gains must be reduced so that the control system does not overshoot, oscillate or hunt about the control set point value. PID controllers have difficulties in the presence of nonlinearities, may trade-off regulation versus response time, do not react to changing process behaviour (say, the process changes after it has warmed up), and have lag in responding to large disturbances. 38

39 PID Controllers - Limitations & Solutions While PID control is the best controller with no model of the process, better performance can be obtained by incorporating a model of the process. The most significant improvement is to incorporate feedforward control with knowledge about the system, and using the PID only to control error. PIDs can also be modified in more minor ways, such as by changing the parameters (either gain scheduling in different use cases or adaptively modifying them based on performance), improving measurement (higher sampling rate, precision, and accuracy, and low-pass filtering if necessary), or cascading multiple PID controllers. 39

40 Design of Control Systems - Process Objectives To aid the product or process - the mechanism, the robot, the chemical plant, the aircraft, etc to do its job. Optimize performance for stability, disturbance regulation, tracking accuracy or reduction of the effects of parameter variations. 40

41 Design of Control Systems - Process Steps Understand the process and its performance requirements. Select the number and type of sensor(s) considering the location, technology and noise. Select the number and types of actuators considering the location, technology, noise and power. Develop a linear model of the process, actuator and sensor. Design a compensated controller. Test, modify and re-test. Feedback Control of dynamic Systems, Franklin, Powell and Emami-Naeini, 2006 Prentice-Hall. 41

42 Design of Control Systems - Analogue Systems Typically consist of an operational amplifier based active filter with either lowpass or bandpass characteristics. Responses are governed by available filter types - Bessel, Chebyshev and Butterworth. Low pass compensators also known as lag compensators or PI (Proportional Integral) controllers. Bandpass networks are referred as lead-lag compensators or PID controllers. 42

43 Design of Control Systems - Digital Systems Desired Response Error Digital Controller Response Digital-to- Analogue COnverter Plant Response Output Response Analogueto-Digital Converter Feedback Sensor Response Digital Systems typically mimic analogue varieties. Exception is that of data conversion of both controller output and feedback signals. 43

44 Design of Control Systems - Digital Systems Advantages Easier implementation, since responses can be programmed. Parameter drift is eliminated. Changes are easy and almost always require no circuit modifications. Reductions in size, power, weight and cost. Reliability (easier testing and verification regimes). 44

45 Design of Control Systems - Digital Systems Digital controllers, in addition to PID, provide additional algorithms. Notch Filter Notch filters are used to control mechanical resonances in a plant. Dead Beat Controller Deadbeat controller provides very short settling times in a control system by replacing all of the poles in the systems with poles at the origin. Adaptive Filter Adaptive filters are useful when plant response cannot be determined due to insufficient information or if it is subjected to time varying change. Can also be used to characterize an unknown plant. 45

46 Algorithm Implementation Considerations Digital processing systems operate using sampled data instead of continuous data as is used in analogue systems. Mathematically, differential equations are used to model DSP functions. A DSP contains a MAC or Multiply-Accumulate Instruction. This allows the multiplication of one variable by another and the subsequent summation of the resulting product with the accumulator, all operations occurring in one processor cycle. This fact makes DSP processors ideal candidates for medium to high performance embedded control applications requiring computation intensive processing. Two of the most important building blocks of DSP are FIT and the IIR filters. 46

47 Algorithm Implementation Considerations - FIR Filter "FIR" means "Finite Impulse Response". They can easily be designed to be "linear phase" (and usually are). Put simply, linear-phase filters delay the input signal but don t distort its phase. They are simple to implement. On most DSP microprocessors, the FIR calculation can be done by looping a single instruction. They are suited to multi-rate applications. FIR Filters are feedforward filters where the output values are a function of a finite number of past input values. FIR filters tend to be used where pass band characteristics are specified. These include the start and end of passband and ripple. 47

48 Algorithm Implementation Considerations - IIR Filter IIR means "Infinite Impulse Response". The impulse response is "infinite" because there is feedback in the filter. IIR filters can achieve a given filtering characteristic using less memory and calculations than a similar FIR filter. They are however more susceptible to problems of finite-length arithmetic, such as noise generated by calculations, and limit cycles. They are harder (slower) to implement using fixed-point arithmetic. They don't offer the computational advantages of FIR filters for multirate (decimation and interpolation) applications. 48

49 Algorithm Implementation Considerations - Filter Comparison IIR More efficient Analog equivalent May be unstable Non-liner phase response No efficiency gained by decimation FIR Less efficient No analog equivalent Always stable Linear phase response Decimation increases efficiency 49

50 Control System Hardware Implementation Desired Response Input Code/Data Memory Sensor Input condition Digital Signal Processor Output condition Driver Plant Functional Diagram of a DSP based Controller The heart of the controller is the processor. It is often single chip device. There are three types: microcontrollers, microprocessors DSPs. 50

51 Control System Hardware Implementation - Microcontrollers Microcontrollers are single chip devices with a low to medium performance core, a basic for of I/O (input/output), memory. Processors do not contain any inherent mathematical type functions. complex operations must be performed with simpler arithmetic, logical, and data move functions. Can be used in low performance applications. best suited for high volume, simple function control systems that do not demand high performance. Cost is relatively low. Examples are: PIC family, Motorola 68H series and Intel 8051 series. 51

52 Control System Hardware Implementation - Microprocessors Microprocessors are generally low to high performance devices that rely on external I/O peripherals and memory for proper operation. Operational speeds are higher than microcontrollers. More complex instructions are available as well as some floating point arithmetic on the chip or as a processor. can be used in low to moderate performance control systems, including ancillary functions such as human-machine interfaces. Cost is moderate to high. Examples are Motorola s and Power PC families; AMD Opteron family, Cypress Semiconductor PSoC family and Intel i960 family. 52

53 Control System Hardware Implementation - DSPs DSPs are high performance processors, optimized for computational efficiency. built in ports for interfacing with ADCs and DACs and other processors. Data can be represented in fixed point or floating point formats. Programs can be loaded from eternal memory. Handle moderate to high performance control systems. Cost is low to moderate. Examples are Analog Devices 21xx family, Texas Instruments C6000 family and Motorola family. 53

54 Control System Hardware Implementation - Factors Sensor input dynamic range, sampling rate, number of sensor inputs interface, polled or interrupt driven. Control algorithm fixed point or floating point, computational performance requirements, data storage requirement, program storage requirement. Controller output considerations Same as sensor input. Cost and Schedule COST versus product development. 54

55 Control System Hardware Implementation - Typical Plant Parameters Parameter Position Speed Acceleration Sensing Method Potentiometer (linear or angular) LVDT (Linear) Resolver (Angular) Optical Encoder (Linear or Angular) Tachometer (RPM to voltage) Hall Effect (Frequency) Optical Encoder (Frequency) Piezoelectric Accelerometer Strain Gage Accelerometer 55

56 Control System Hardware Implementation - Typical Plant Parameters Parameter Temperature Pressure Force Flow Rate Sensing Method Thermocouple Semiconductor Junction Thermisotr Strain Gage Piezoelectric Force Transducer Strain Gage Piezoelectric Force Transducer Differential Pressure Impeller (Frequency) Thermal (Differential temperature) 56

57 Control System Hardware Implementation - Factors LNA BPF VGA ADC Data to DSP DSP Input Schematic Diagram VGA Control from DSP Input Signal Conditioning Amplification of sensor signals, filtered, variable gain amplifier for adjustment, analogue to digital converter and interface to DSP. Controller Response local control - no operator inout control by another processor through a port - interactive control 57

58 Control System Hardware Implementation - Factors DAC LPF Buffer PA Data from DSP Plant Drive PA Control from DSP PWM Plant Drive DSP Output Schematic Diagram Controller Output Output signal is converted back to analogue signal with a DAC, filtered, fed to power amplifier. 58

59 Control System Hardware Implementation - Method Translate the system requirements into a design specification Translate the design specification into a functional block diagram. Optimize the block diagram. Translate the block diagram into a mathematical model. Optimize the mathematical model. 59

60 Control System Hardware Implementation - Block Diagram 60

61 Control System Hardware Implementation - Case Studies Computer Hard disk Control System. This case study demonstrates the ability to perform classical digital control design by going through the design of a computer hard-disk read/write head position controller. Automobile Active Suspension System. The vehicle suspension system is responsible for driving comfort and safety as the suspension caries the vehicle body and transmits all forces between the body and the road. By adding an active suspension comfort and safety are considerably improved compared to suspension setups with fixed properties. 61

62 Hard Disc Drive Description 62

63 Hard Disc Drive Description Disk read/write heads are the small parts of a disk drive, that move above the disk platter and transform platter's magnetic field into electrical current (read the disk) or vice versa transform electrical current into magnetic field (write the disk) They are high-precision, high- performance machines produced in very high volumes and sold at relatively low cost. 63

64 Performance of a Hard Disc Drive There are three ways to measure the performance of a hard disk: Data Rate The data rate is the number of bytes per second that the drive can deliver to the CPU. Rates between 5 and 40 megabytes per second are common. Seek Time The seek time is the amount of time between when the CPU requests a file and when the first byte of the file is sent to the CPU. Times between 10 and 20 milliseconds are common. Capacity - The other important parameter is the capacity of the drive, which is the number of bytes it can hold. 64

65 65

66 Hard Disc Drive Description In a hard drive, the heads 'fly' above the disk surface with clearance of as little as 3 nanometres. The "flying height" is constantly decreasing to enable higher areal density. The flying height of the head is controlled by the design of an air-bearing etched onto the disk-facing surface of the slider. The role of the air bearing is to maintain the flying height constant as the head moves over the surface of the disk. If the head hits the disk's surface, a catastrophic head crash can result. 66

67 Hard Disc Drive Construction Details Platters of a Hard disc Hard disc head The microphotograph of the head shows that the size of the front face is about 0.3 mm. One functional part of the head is the round, orange structure in the middle - the lithographically defined copper coil of the write transducer. 67

68 Hard Disc Drive Description The plates are manufactured to amazing tolerances and are mirrorsmooth and typically spin at 3,600 or 7,200 rpm when the drive is operating. The light and fast-moving arm holds the read/write heads and is controlled by the voice-coil actuator. The arm is able to move the heads from the hub to the edge of the drive and can do this, back and forth, up to 50 times per second. In order to keep the magnetic head as close to the disk surface as possible, a self-pressurized air-bearing design is used for the sliders. 68

69 Design Challenges To design each of the four main components of the disk drive servo system plant dynamics, sensors, actuators, and control algorithms and to reduce the effect of mechanical disturbances to the drive. Disturbances arise from many sources: external shocks and vibrations, mechanical imperfections in the bearings of the disk spindle, disk vibrations, turbulent flow over the actuator due to air currents generated by the rapidly spinning disks, and the occasional contact between the slider and the disk. Plant dynamics which affect servo performance are mechanical resonances in the suspension (the leaf spring that holds the head against the disk), the actuator arm, the pivot bearing, and the voice coil. 69

70 Design Challenges The pivot bearing also has nonlinear friction dynamics, which include hysteresis and which primarily affect seek performance. Flutter vibration modes in the disks and other modes in the spindle contribute to tracking errors by moving the data track relative to an inertial frame of reference. Noise and distortion are two other important sources of tracking error in disk drives. Noise arises not only from electronics, but also from the magnetic media. The magnetoresistive head readers are nonlinear devices. Quantization noise is, of course, present in this digital control system. 70

71 Functional Block Diagram 71

72 Computer Hard Disc Drive - Transfer Function Using Newton's law, a simple model for the read/write head is the differential equation: where J is the inertia of the head assembly, C is the viscous damping coefficient of the bearings, K is the return spring constant, Ki is the motor torque constant, θ is the angular position of the head, and i is the input current. Taking the Laplace transform, the transfer function from i to θ is Using the values J = 0.01 kg m 2, C = Nm/(rad/sec), K = 10 Nm/ rad, and Ki = 0.05 Nm/rad, form the transfer function description of this system. Transfer function: 72

73 Control System Performance Step Response with large Phase margin Step response with filter Step response with controller implemented 73

74 Active Suspension Systems - Introduction Active or adaptive suspension technology controls the vertical movement of the wheels with an onboard system rather than the movement being determined entirely by the road surface. The system virtually eliminates body roll and pitch variation in many driving situations including cornering, accelerating, and braking. This technology allows car manufacturers to achieve a greater degree of ride quality and car handling by keeping the tires perpendicular to the road in corners, allowing better traction and control. 74

75 Control System Hardware Implementation - Active Suspension Systems Studies The vehicle suspension system is responsible for driving comfort and safety as the suspension caries the vehicle body and transmits all forces between the body and the road. Active systems enable the suspension system to adapt to various driving conditions. By adding a variable damper and/or spring, driving comfort and safety are considerably improved compared to suspension setups with fixed properties. 75

76 Suspension Systems - Safety and Stability Issues Safety is the result of a good suspension design in terms of wheel suspension, springing, steering, and braking, and is reflected in an optimal dynamic behaviour of the vehicle. Tire load variation is an indicator for the road contact and can be used for determining a quantitative value for safety. Driving comfort results from keeping the physiological stress that the vehicle occupants are subjected to by vibrations, noise, and climatic conditions down to as low a level as possible. The acceleration of the body is an obvious quantity for the motion and vibration of the car body and can be used for determining a quantitative value for driving comfort. 76

77 Suspension Systems - Conflicting Criteria In order to improve the ride quality, it is necessary to isolate the body. To improve the ride stability, it is important to keep the tire in contact with the road surface. For a given suspension spring, the better isolation of the sprung mass from road disturbances can be achieved with a soft damping by allowing a larger suspension deflection. Better road contact can be achieved with a hard damping preventing unnecessary suspension deflections. Therefore, the ride quality and the drive stability are two conflicting criteria. 77

78 Suspension Systems - Currently Available Systems Currently three types of vehicle suspensions are used: passive, semi-active, and active. Systems implemented in automobiles today are based on hydraulic or pneumatic operation. These solutions do not satisfactorily solve the vehicle oscillation problem, or they are very expensive and increase the vehicle s energy consumption. Significant improvement of suspension performance is achieved by active systems, however, they are expensive and complex. 78

79 Suspension Systems - Model Inputs The system has ten inputs, six of which are exogenous and the others controllable. These inputs are: Exogenous: The road velocity inputs experienced at each wheel Vehicle pitch force (due to accelerating/braking/cornering the vehicle) Vehicle roll input (due to cornering the vehicle) Controllable: Actuator forces applied to the suspension system at each corner of the vehicle. Outputs The ride quality can be quantified by examining the vertical and angular accelerations of the vehicle body, as well as the ability for the vehicle to remain level regardless of operating conditions. 79

80 Suspension Systems - Operating Scenarios for modelling 1. Driving over a speed bump (generates a vertical velocity profile input). 2. Braking at 1 g by applying the appropriate pitch moment to the vehicle centre of gravity. 3. Cornering by applying the appropriate pitch and roll moment to the vehicle centre of gravity. 80

81 Suspension Systems - LQR Models Design of an LQR Control Strategy for Implementation on a Vehicular Active Suspension System Ben Creed, Nalaka Kahawatte, Scott Varnhagen 2010 University of California, Davis 81

82 Suspension Systems - Bose System The Bose system uses a linear electromagnetic motor (LEM) at each wheel in lieu of a conventional shock- and-spring setup. Amplifiers provide electricity to the motors in such a way that their power is regenerated with each compression of the system. The main benefit of the motors is that they are not limited by the inertia inherent in conventional fluid-based dampers. As a result, an LEM can extend and compress at a much greater speed, virtually eliminating all vibrations in the passenger cabin. The wheel's motion can be so finely controlled that the body of the car remains level regardless of what's happening at the wheel. The LEM can also counteract the body motion of the car while accelerating, braking, and cornering, giving the driver a greater sense of control. 82

83 Bose Active Suspension System 83

84 Active Suspension Systems Bose Active Suspension 84

85 Active Suspension Systems The Siemens ecorner project The ecorner concept replaces the conventional wheel suspension with hydraulic shock absorbers, mechanical steering, hydraulic brakes and internal combustion engines with integrated in-wheel systems 85

86 Resources Feedback Control of Dynamic Systems, Gene Franklin, David Powell and Abbas Emami-Naeini, Pearson Prentice-Hall, Feedback Control Systems, Charles Phillips and Royce Harbour, Prentice-Hall, Mechatronics, G.S. Hegde, Jones and Bartlett Publishers LLC, Introduction to Mechatronics and Measurement Systems, A. Alciatore and M. Histand, McGraw Hill,

87 Lecture 1: Introduction to Smart Materials and Systems Lecture 2: Sensor technologies for smart systems and their evaluation criteria. Lecture 3: Actuator technologies for smart systems and their evaluation criteria. Lecture 4: Piezoelectric Materials and their Applications. Lecture 5: Control System Technologies. Lecture 6: Smart System Applications. S. Eswar Prasad, Adjunct Professor, Department of Mechanical & Industrial Engineering, Chairman, Piemades Inc, Piemades, Inc. 87