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E-raamat: PID Control System Design and Automatic Tuning using MATLAB/Simulink

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  • Formaat: PDF+DRM
  • Sari: IEEE Press
  • Ilmumisaeg: 24-Feb-2020
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9781119469377
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PID Control System Design and Automatic Tuning using MATLAB/SimulinkSuurem pilt
  • Formaat: PDF+DRM
  • Sari: IEEE Press
  • Ilmumisaeg: 24-Feb-2020
  • Kirjastus: Wiley-IEEE Press
  • Keel: eng
  • ISBN-13: 9781119469377
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Covers PID and state space control systems from the very basics to the advanced topics

This book covers the design and implementation of PID and state space control systems with operational constraints. It provides students, researchers, and industrial practitioners with everything they need to know about PID control systems—from classical tuning rules and model-based design to constraints, automatic tuning, cascade control, and gain scheduled control. Along similar lines to PID control, state space control systems are introduced with emphasis on process control applications such as disturbance rejection, reference following, and operational constraints.  

PID and State Space Control Systems: Design and Implementation using MATLAB/Simulink is comprised of two parts. Part 1 introduces PID control system structures, sensitivity analysis, PID control design, implementation, disturbance observer-based PID control, gain scheduled PID control system, cascade PID control systems, frequency domain-based PID control design, and automatic tuning. It also presents resonant control systems relevant to many engineering applications. The implementation of PID control and resonant control highlights how to deal with operational constraints. Part 2 introduces state space control systems, including state space models, state feedback controller design, observer design, observer error analysis, linear quadratic regulator design, linear quadratic regulator with prescribed degree of stability, and state estimate feedback control. Sharing the same common ground with PID control, and resonant control, Part 2 also presents state space control systems that have the capabilities for disturbance rejection, reference following, and effectively dealing with operational constraints. Both Part 1 and Part 2 contain many original results obtained by the author.

  • Provides unique coverage of PID Control of unmanned aerial vehicles (UAVs), including mathematical models of multi-rotor UAVs, control strategies of UAVs, and automatic tuning of PID controllers for UAVs
  • Provides detailed descriptions of automatic tuning of PID control systems, including relay feedback control systems, frequency response estimation, Monte-Carlo simulation studies, PID controller design using frequency domain information, and MATLAB/Simulink simulation and implementation programs for automatic tuning
  • Includes 19 MATLAB tutorials, in a step-by-step manner, to illustrate the design, simulation and implementation of PID, and state space control systems
  • Assists lecturers, teaching assistants, students, and other readers to learn PID and state space control with constraints and apply the control theory to various areas
  • Supplemented with lecture slides and MATLAB/ Simulink programs featured on the MathWorks website and Wiley’s website

PID and State Space Control Systems: Design and Implementation using MATLAB/Simulink is intended for upper-level undergraduate electrical, chemical, mechanical, and aerospace engineering students, and will greatly benefit postgraduate students, researchers, and industrial personnel who work with control systems and their applications.  

Preface xv
Acknowledgment xvii
List of Symbols and Acronyms
xix
About the Companion Website xxi
1 Basics of PID Control
1(30)
1.1 Introduction
1(1)
1.2 PID Controller Structure
1(12)
1.2.1 Proportional Controller
1(2)
1.2.2 Proportional Plus Derivative Controller
3(2)
1.2.3 Proportional Plus Integral Controller
5(4)
1.2.4 PID Controllers
9(3)
1.2.5 The Commercial PID Controller Structure
12(1)
1.2.6 Food for Thought
13(1)
1.3 Classical Tuning Rules for PID Controllers
13(5)
1.3.1 Ziegler-Nichols Oscillation Based Tuning Rules
13(2)
1.3.2 Tuning Rules based on the First Order Plus Delay Model
15(2)
1.3.3 Food for Thought
17(1)
1.4 Model Based PID Controller Tuning Rules
18(3)
1.4.1 IMC-PID Controller Tuning Rules
18(1)
1.4.2 Padula and Visioli Tuning Rules
19(1)
1.4.3 Wang and Cluett Tuning Rules
20(1)
1.4.4 Food for Thought
21(1)
1.5 Examples for Evaluations of the Tuning Rules
21(6)
1.5.1 Examples for Evaluating the Tuning Rules
21(4)
1.5.2 Fired Heater Control Example
25(2)
1.6 Summary
27(1)
1.7 Further Reading
28(3)
Problems
28(3)
2 Closed-loop Performance and Stability
31(40)
2.1 Introduction
31(1)
2.2 Routh---Hurwitz Stability Criterion
31(5)
2.2.1 Determining Closed-loop Poles
32(1)
2.2.2 Routh---Hurwitz Stability Criterion
33(3)
2.2.3 Food for Thought
36(1)
2.3 Nyquist Stability Criterion
36(6)
2.3.1 Nyquist Diagram
36(2)
2.3.1.1 Gain Margin
38(1)
2.3.1.2 Phase Margin
38(1)
2.3.1.3 Delay Margin
38(2)
2.3.2 Rework of Tuning Rules based PID Controllers
40(2)
2.3.3 Food for Thought
42(1)
2.4 Control System Structures and Sensitivity Functions
42(5)
2.4.1 One Degree of Freedom Control System Structure
43(1)
2.4.2 Two Degrees of Freedom Design
44(1)
2.4.2.1 Two degrees of freedom implementation of PI controllers
45(1)
2.4.3 Sensitivity Functions in Feedback Control
45(2)
2.4.4 Food for Thought
47(1)
2.5 Reference Following and Disturbance Rejection
47(7)
2.5.1 Closed-loop Bandwidth
47(3)
2.5.2 Reference Following and Disturbance Rejection with PID Controllers
50(3)
2.5.3 Reference Following and Disturbance Rejection with Resonant Controllers
53(1)
2.5.4 Food for Thought
54(1)
2.6 Disturbance Rejection and Noise Attenuation
54(5)
2.6.1 Conflict between Disturbance Rejection and Noise Attenuation
54(1)
2.6.2 PID Controller for Disturbance Rejection and Noise Attenuation
55(3)
2.6.3 Food for Thought
58(1)
2.7 Robust Stability and Robust Performance
59(6)
2.7.1 Modeling Errors
59(1)
2.7.2 Robust Stability
60(2)
2.7.3 Case Study: Robust Control of Polymer Reactor
62(3)
2.7.4 Food for Thought
65(1)
2.8 Summary
65(2)
2.9 Further Reading
67(4)
Problems
67(4)
3 Model-Based PID and Resonant Controller Design
71(42)
3.1 Introduction
71(1)
3.2 PI Controller Design
71(7)
3.2.1 Desired Closed-loop Performance Specification
71(1)
3.2.2 Model and Controller Structures
72(3)
3.2.3 Closed-loop Transfer Functions for Different Configurations
75(2)
3.2.4 Food for Thought
77(1)
3.3 Model Based Design for PID Controllers
78(18)
3.3.1 PD Controller Design
78(3)
3.3.2 Analytical Examples for Ideal PID with Pole-zero Cancellation
81(3)
3.3.3 Analytical Examples for PID Controllers with Filters
84(8)
3.3.4 PID Controller Design without Pole-Zero Cancellation
92(2)
3.3.5 MATLAB Tutorial on Solution of a PID Controller with Filter
94(1)
3.3.6 Food for Thought
95(1)
3.4 Resonant Controller Design
96(6)
3.4.1 Resonant Controller Design
96(1)
3.4.2 Steady-state Error Analysis
97(2)
3.4.3 Pole-Zero Cancellation in the Design of a Resonant Controller
99(2)
3.4.4 Food for Thought
101(1)
3.5 Feedforward Control
102(6)
3.5.1 Basic Ideas about Feedforward Control
102(1)
3.5.2 Three Springs and Double Mass System
103(5)
3.5.3 Food for Thought
108(1)
3.6 Summary
108(1)
3.7 Further Reading
108(5)
Problems
109(4)
4 Implementation of PID Controllers
113(26)
4.1 Introduction
113(1)
4.2 Scenario of a PID Controller at work
113(1)
4.3 PID Controller Implementation using the Position Form
114(3)
4.3.1 The Steady-state Information Needed
114(1)
4.3.2 Discretization of a PID Controller
115(1)
4.3.3 Food for Thought
116(1)
4.4 PID Controller Implementation using the Velocity Form
117(5)
4.4.1 Discretization of a PI Controller
117(2)
4.4.2 Discretization of a PID Controller using the Velocity Form
119(1)
4.4.3 Improving Accuracy in a Slower Sampling Environment
120(2)
4.4.4 Food for Thought
122(1)
4.5 Anti-windup Implementation using the Position Form
122(4)
4.5.1 Integrator Windup Scenario
122(2)
4.5.2 Anti-windup Mechanisms in the Position Form of PI Controllers
124(1)
4.5.3 Food for Thought
125(1)
4.6 Anti-windup Mechanisms in the Velocity Form
126(4)
4.6.1 Anti-windup Mechanism on the Amplitude of the Control Signal
126(3)
4.6.2 Limits on the Rate of Change of the Control Signal
129(1)
4.6.3 Food for Thought
129(1)
4.7 Tutorial on PID Anti-windup Implementation
130(3)
4.8 Dealing with Other Implementation Issues
133(3)
4.8.1 Plant Start-up
134(1)
4.8.2 Dealing with Quantization Errors in PID Controller Implementation
135(1)
4.9 Summary
136(1)
4.10 Further Reading
137(2)
Problems
137(2)
5 Disturbance Observer-Based PID and Resonant Controller
139(40)
5.1 Introduction
139(1)
5.2 Disturbance observer-Based PI Controller
139(10)
5.2.1 Estimation of Disturbance with Control
139(1)
5.2.1.1 Choice of Proportional Controller K1
140(1)
5.2.1.2 Compensation of Steady-state Error
140(1)
5.2.1.3 The closed-loop poles
141(1)
5.2.1.4 Implementation procedure
142(1)
5.2.2 Equivalence to PI controller
143(1)
5.2.3 MATLAB Tutorial for Implementation of a PI Controller via Estimation
144(1)
5.2.4 Examples for Estimator based PI Controllers
145(3)
5.2.5 Food for Thought
148(1)
5.3 Disturbance observer-Based PID Controller
149(7)
5.3.1 Proportional Plus Derivative Control
149(1)
5.3.2 Adding Integral Action
150(1)
5.3.3 Equivalence to a PID Controller
151(2)
5.3.4 MATLAB Tutorial on the Implementation of a disturbance observer-based PID Controller
153(2)
5.3.5 Examples for Disturbance observer-based PID Controller
155(1)
5.3.6 Food for Thought
156(1)
5.4 Disturbance observer-Based Resonant Controller
156(11)
5.4.1 Resonant Controller Design
156(2)
5.4.2 Resonant Controller Implementation
158(1)
5.4.3 Equivalence to a Resonant Controller
159(1)
5.4.4 MATLAB Tutorial on Disturbance observer-Based Resonant Controller Implementation
160(2)
5.4.5 Examples for Disturbance observer-Based Resonant Controllers
162(5)
5.4.6 Food for Thought
167(1)
5.5 Multi-frequency Resonant Controller
167(5)
5.5.1 Adding Integral Action to the Resonant Controller
168(2)
5.5.2 Adding More Periodic Components
170(1)
5.5.3 Food for Thought
171(1)
5.6 Summary
172(1)
5.7 Further Reading
172(7)
Problems
173(6)
6 PID Control of Nonlinear Systems
179(24)
6.1 Introduction
179(1)
6.2 Linearization of the Nonlinear Model
179(8)
6.2.1 Approximation of a Nonlinear Function
179(2)
6.2.2 Linearization of nonlinear differential equations
181(1)
6.2.3 Case Study: Linearization of the Coupled Tank Model
181(3)
6.2.4 Case Study: Linearization of the Induction Motor Model
184(2)
6.2.5 Food for Thought
186(1)
6.3 Case Study: Ball and Plate Balancing System
187(1)
6.3.1 Dynamics of the Ball and Plate Balancing System
187(1)
6.3.2 Linearization of the Nonlinear Model
188(1)
6.3.3 PID Controller Design
189(1)
6.3.4 Implementation and Experimental Results
190(1)
6.3.4.1 Disturbance Rejection
191(1)
6.3.4.2 Making a Square Movement
192(1)
6.3.4.3 Making a Circle Movement
192(2)
6.3.4.4 Making more Complicated Movements
194(1)
6.3.5 Food for Thought
194(1)
6.4 Gain Scheduled PID Control Systems
194(5)
6.4.1 The Weighting Parameters
194(2)
6.4.2 Gain Scheduled Implementation using PID Velocity Form
196(1)
6.4.3 Gain Scheduled Implementation using an Estimator Based PID Controller
197(2)
6.4.4 Food for Thought
199(1)
6.5 Summary
199(1)
6.6 Further Reading
199(4)
Problems
200(3)
7 Cascade PID Control Systems
203(30)
7.1 Introduction
203(1)
7.2 Design of a Cascade PID Control System
203(6)
7.2.1 Design Steps for a Cascade Control System
203(1)
7.2.2 Simple Design Examples
204(4)
7.2.3 Achieving Closed-loop Performance Invariance (Approximate) in a Cascade Structure
208(1)
7.2.4 Food for Thought
209(1)
7.3 Cascade Control System for Input Disturbance Rejection
209(5)
7.3.1 Frequency Characteristics for Disturbance Rejection
210(1)
7.3.2 Simulation Studies
211(2)
7.3.3 Food for Thought
213(1)
7.4 Cascade Control System for Actuator Nonlinearities
214(16)
7.4.1 Cascade Control for Actuator with a Deadzone
214(4)
7.4.2 Cascade Control for Actuators with Quantization Errors
218(3)
7.4.3 Cascade Control for Actuators with Backlash Nonlinearity
221(6)
7.4.4 Food for Thought
227(3)
7.5 Summary
230(1)
7.6 Further Reading
230(3)
Problems
231(2)
8 PID Controller Design for Complex Systems
233(26)
8.1 Introduction
233(1)
8.2 PI Controller Design via Gain and Phase Margins
233(5)
8.2.1 PI Controller Design Using Gain Margin and Phase Margin Specifications
233(1)
8.2.2 Design Examples
234(4)
8.2.3 Food for Thought
238(1)
8.3 PID Controller Design using Two Frequency Points
238(11)
8.3.1 Finding the PID Controller Parameters
238(2)
8.3.2 Desired Closed-loop Performance Specification using Two Frequency Points
240(2)
8.3.3 Design Examples
242(1)
8.3.4 MATLAB Tutorial on PID Controller Design Using two Frequency Points
243(2)
8.3.5 PID Controller Design for Beer Filtration Process
245(3)
8.3.6 Food for Thought
248(1)
8.4 PID Controller Design for Integrating Systems
249(7)
8.4.1 The Approximate Model
249(1)
8.4.2 Selection of Desired Closed-loop Performance
250(1)
8.4.3 Normalization of the Parameters and Empirical Rules
251(2)
8.4.4 Gain and Phase Margins
253(1)
8.4.5 Simulation Examples
253(3)
8.4.6 Food for Thought
256(1)
8.5 Summary
256(1)
8.6 Further Reading
257(2)
Problems
257(2)
9 Automatic Tuning of PID Controllers
259(46)
9.1 Introduction
259(1)
9.2 Relay Feedback Control
259(8)
9.2.1 Relay Control with Hysteresis
259(4)
9.2.2 Relay Control with Integrator
263(4)
9.2.3 Food for Thought
267(1)
9.3 Estimation of Frequency Response using the Fast Fourier Transform (FFT)
267(6)
9.3.1 FFT Estimation
268(1)
9.3.2 MATLAB Tutorial using the FFT for Estimation
269(1)
9.3.3 Monte-Carlo Simulation Studies
270(2)
9.3.4 Food for Thought
272(1)
9.4 Estimation of Frequency Response Using the frequency sampling filter (FSF)
273(6)
9.4.1 Frequency Sampling Filter Model
273(3)
9.4.2 MATLAB Tutorial on Estimation Using the FSF Model
276(2)
9.4.3 Monte-Carlo Simulation using the FSF Estimation
278(1)
9.4.4 Food for Thought
279(1)
9.5 Monte-Carlo Simulation Studies
279(4)
9.5.1 Effect of Unknown Constant Disturbance
279(1)
9.5.2 Effect of Unknown Low Frequency Disturbance
280(2)
9.5.3 Estimation of the Steady-state Value
282(1)
9.5.4 Food for Thought
283(1)
9.6 Auto-tuner Design for Stable Plant
283(8)
9.6.1 MATLAB Tutorial on Auto-tuner for Stable Plant
284(2)
9.6.2 Evaluation of the Auto-tuner for a Stable Plant
286(1)
9.6.2.1 PID Controller Parameters
287(1)
9.6.2.2 Nyquist Plots
287(1)
9.6.2.3 Closed-loop Simulation Results
288(1)
9.6.3 Comparative Studies
289(1)
9.6.4 Food for Thought
290(1)
9.7 Auto-tuner Design for a Plant with an Integrator
291(9)
9.7.1 Estimation of an Integrating Plus Delay Model
291(1)
9.7.2 Auto-tuner for Integrating Systems
292(5)
9.7.3 Auto-tuning of Cascade Control Systems
297(3)
9.7.4 Food for Thought
300(1)
9.8 Summary
300(1)
9.9 Further Reading
301(4)
Problems
302(3)
10 PID Control of Multi-rotor Unmanned Aerial Vehicles
305(22)
10.1 Introduction
305(1)
10.2 Multi-rotor Dynamics
305(6)
10.2.1 Dynamic Models for Attitude Control
305(2)
10.2.2 Actuator Dynamics for Quadrotor UAVs
307(2)
10.2.3 Actuator Dynamics of Hexacopters
309(2)
10.2.4 Food for Thought
311(1)
10.3 Cascade Attitude Control of Multi-rotor UAVs
311(2)
10.3.1 Linearized Model for the Secondary Plant
312(1)
10.3.2 Linearized Model for the Primary Plant
313(1)
10.3.3 Food for Thought
313(1)
10.4 Automatic Tuning of Attitude Control Systems
313(11)
10.4.1 Test Rigs for Auto-tuning Cascade PI Controllers of Multi-rotor UAVs
314(1)
10.4.2 Experimental Results for Quadrotor UAV
314(6)
10.4.3 Experimental Results for Hexacopter
320(4)
10.4.4 Food for Thought
324(1)
10.5 Summary
324(1)
10.6 Further Reading
325(2)
Problems
325(2)
Suggestions to Food for Thought Questions 327(4)
Bibliography 331(10)
Index 341
LIUPING WANG, PHD, is a Professor at RMIT University in Australia. An electrical engineer by training, Professor Wang gained substantial process control experience by working in the Chemical Engineering Department at the University of Toronto, Canada, and the Center for Integrated Dynamics at the University of Newcastle, Australia. She is the author of four books in the areas of model predictive control, control systems for electric drives and power converters, system identification, and PID control.
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