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State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials [Hardback]

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  • Formāts: Hardback, 448 pages, height x width x depth: 244x170x29 mm, weight: 794 g
  • Sērija : IEEE Press
  • Izdošanas datums: 03-Nov-2022
  • Izdevniecība: Wiley-IEEE Press
  • ISBN-10: 1119694639
  • ISBN-13: 9781119694632
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State Feedback Control and Kalman Filtering with MATLAB/Simulink TutorialsPalielināt
  • Formāts: Hardback, 448 pages, height x width x depth: 244x170x29 mm, weight: 794 g
  • Sērija : IEEE Press
  • Izdošanas datums: 03-Nov-2022
  • Izdevniecība: Wiley-IEEE Press
  • ISBN-10: 1119694639
  • ISBN-13: 9781119694632
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781119694632.html
Keywords:
STATE FEEDBACK CONTROL AND KALMAN FILTERING WITH MATLAB/SIMULINK TUTORIALS

Discover the control engineering skills for state space control system design, simulation, and implementation

State space control system design is one of the core courses covered in engineering programs around the world. Applications of control engineering include things like autonomous vehicles, renewable energy, unmanned aerial vehicles, electrical machine control, and robotics, and as a result the field may be considered cutting-edge. The majority of textbooks on the subject, however, lack the key link between the theory and the applications of design methodology.

State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials provides a unique perspective by linking state space control systems to engineering applications. The book comprehensively delivers introductory topics in state space control systems through to advanced topics like sensor fusion and repetitive control systems. More, it explores beyond traditional approaches in state space control by having a heavy focus on important issues associated with control systems like disturbance rejection, reference tracking, control signal constraint, sensor fusion and more. The text sequentially presents continuous-time and discrete-time state space control systems, Kalman filter and its applications in sensor fusion.

State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials readers will also find:

  • MATLAB and Simulink tutorials in a step-by-step manner that enable the reader to master the control engineering skills for state space control system design and Kalman filter, simulation, and implementation
  • An accompanying website that includes MATLAB code
  • High-end illustrations and tables throughout the text to illustrate important points
  • Written by experts in the field of process control and state space control systems

State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials is an ideal resource for students from advanced undergraduate students to postgraduates, as well as industrial researchers and engineers in electrical, mechanical, chemical, and aerospace engineering.

Author Biography xiii
Preface xv
Acknowledgments xxi
List of Symbols and Acronyms
xxiii
About the Companion Website xxv
Part I Continuous-time State Feedback Control
1(126)
1 State Feedback Controller and Observer Design
3(64)
1.1 Introduction
3(1)
1.2 Motivation for Going Beyond PID Control
4(8)
1.3 Basics in State Feedback Control
12(9)
1.3.1 State Feedback Control
12(6)
1.3.2 Controllability
18(3)
1.3.3 Food for Thought
21(1)
1.4 Pole-assignment Controller
21(8)
1.4.1 The Design Method
21(3)
1.4.2 Similarity Transformation for Controller Design
24(3)
1.4.3 MATLAB Tutorial on Pole-assignment Controller
27(2)
1.4.4 Food for Thought
29(1)
1.5 Linear Quadratic Regulator (LQR) Design
29(18)
1.5.1 Motivational Example
29(3)
1.5.2 Linear Quadratic Regulator Design
32(2)
1.5.3 Selection of Q and R Matrices
34(5)
1.5.4 LQR with Prescribed Degree of Stability
39(7)
1.5.5 Food for Thought
46(1)
1.6 Observer Design
47(11)
1.6.1 Motivational Example for Observer
47(3)
1.6.2 Observer Design
50(3)
1.6.3 Observability
53(2)
1.6.4 Duality between Controller and Observer
55(1)
1.6.5 Observer Implementation
56(1)
1.6.6 Food for Thought
57(1)
1.7 State Estimate Feedback Control System
58(3)
1.7.1 State Estimate Feedback Control
58(1)
1.7.2 Separation Principle
59(1)
1.7.3 Food for Thought
60(1)
1.8 Summary
61(1)
1.9 Further Reading
62(5)
Problems
63(4)
2 Practical Multivariable Controllers in Continuous-time
67(60)
2.1 Introduction
67(1)
2.2 Practical Controller I: Integral Action via Controller Design
68(24)
2.2.1 The Original Control Law
68(1)
2.2.2 Integrator Windup Scenarios
69(2)
2.2.3 Proposed Practical Multivariable Controller
71(3)
2.2.4 Anti-windup Implementation
74(3)
2.2.5 MATLAB Tutorial on Design and Implementation
77(8)
2.2.6 Application to Drum Boiler Control
85(6)
2.2.1 Food for Thought
91(1)
2.3 Practical Controller II: Integral Action via Observer Design
92(15)
2.3.1 Integral Control via Disturbance Estimation
92(3)
2.3.2 Anti-windup Mechanism
95(1)
2.3.3 MATLAB Tutorial on Design and Implementation
96(6)
2.3.4 Application to Sugar Mill Control
102(1)
2.3.5 Design for Systems with Known States
103(3)
2.3.6 Food for Thought
106(1)
2.4 Drive Train Control of a Wind Turbine
107(14)
2.4.1 Modelling of Wind Turbine's Drive Train
107(3)
2.4.2 Configuration of The Control System
110(1)
2.4.3 Design Method I
111(4)
2.4.4 Design Method II
115(1)
2.4.5 MATLAB Tutorial on Design Method II
116(5)
2.4.6 Food for Thought
121(1)
2.5 Summary
121(1)
2.6 Further Reading
122(5)
Problems
122(5)
Part II Discrete-time State Feedback Control
127(182)
3 Introduction to Discrete-time Systems
129(32)
3.1 Introduction
129(1)
3.2 Discretization of Continuous-time Models
130(12)
3.2.1 Sampling of a Continuous-time Model
130(3)
3.2.2 Stability of Discrete-time System
133(1)
3.2.3 Examples of Discrete-time Models from Sampling
134(7)
3.2.4 Food for Thoughts
141(1)
3.3 Input and Output Discrete-time Models
142(7)
3.3.1 Input and Output Models
142(2)
3.3.2 Finite Impulse Response and Step Response Models
144(4)
3.3.3 Non-minimal State Space Realization
148(1)
3.3.4 Food for Thought
148(1)
3.4 Z-Transforms
149(6)
3.4.1 Z-Transforms for Commonly Used Signals
149(3)
3.4.2 Z-Transfer Functions
152(2)
3.4.3 Food for Thought
154(1)
3.5 Summary
155(1)
3.6 Further Reading
156(5)
Problems
156(5)
4 Discrete-time State Feedback Control
161(34)
4.1 Introduction
161(1)
4.2 Discrete-time State Feedback Control
161(6)
4.2.1 Basic Ideas
161(4)
4.2.2 Controllability in Discrete-time
165(2)
4.2.3 Food for Thought
167(1)
4.3 Discrete-time Observer Design
167(6)
4.3.1 Basic Ideas about Discrete-time Observer
167(4)
4.3.2 Observability in Discrete-time
171(2)
4.3.3 Food for Thought
173(1)
4.4 Discrete-time Linear Quadratic Regulator (DLQR)
173(4)
4.4.1 Objective Function for DLQR
173(1)
4.4.2 Optimal Solution
174(2)
4.4.3 Observer Design using DLQR
176(1)
4.4.4 Food for Thought
176(1)
4.5 Discrete-time LQR with Prescribed Degree of Stability
177(9)
4.5.1 Basic Ideas about a Prescribed Degree of Stability
177(3)
4.5.2 Case Studies
180(6)
4.5.3 Food for Thought
186(1)
4.6 Summary
186(1)
4.7 Further Reading
187(8)
Problems
188(7)
5 Disturbance Rejection and Reference Tracking via Observer Design
195(58)
5.1 Introduction
195(1)
5.2 Disturbance Models
195(5)
5.2.1 Commonly Encountered Disturbance Signals
196(3)
5.2.2 State Space Model with Input Disturbance
199(1)
5.2.3 Food for Thought
200(1)
5.3 Compensation of Input and Output Disturbances in Estimation
200(14)
5.3.1 Motivational Example
200(2)
5.3.2 Input Disturbance Observer Design
202(4)
5.3.3 MATLAB Tutorial for Augmented State Space Model
206(1)
5.3.4 The Observer Error System
207(2)
5.3.5 Output Disturbance Observer Design
209(4)
5.3.6 Food for Thought
213(1)
5.4 Disturbance-Observer-based State Feedback Control
214(9)
5.4.1 The Control Law
214(3)
5.4.2 MATLAB Tutorial for Control Implementation
217(5)
5.4.3 Food for Thought
222(1)
5.5 Analysis of Disturbance-Observer-based Control System
223(10)
5.5.1 Controller Transfer Function
223(2)
5.5.2 Disturbance Rejection
225(2)
5.5.3 Reference Tracking
227(1)
5.5.4 A Case Study
228(4)
5.5.5 Food for Thought
232(1)
5.6 Anti-windup Implementation of the Control Law
233(9)
5.6.1 Algorithm for Anti-windup Implementation
233(3)
5.6.2 Heating Furnace Control
236(3)
5.6.3 Example for Bandlimited Disturbance
239(2)
5.6.4 Food for Thought
241(1)
5.7 Summary
242(1)
5.8 Further Reading
243(10)
Problems
243(10)
6 Disturbance Rejection and Reference Tracking via Control Design
253(56)
6.1 Introduction
253(1)
6.2 Embedding Disturbance Model into Controller Design
254(6)
6.2.1 Formulation of Augmented State Space Model
254(2)
6.2.2 MATLAB Tutorial
256(2)
6.2.3 Controllability and Observability
258(1)
6.2.4 Food for Thought
259(1)
6.3 Controller and Observer Design
260(9)
6.3.1 Controller Design and Control Signal Calculation
260(2)
6.3.2 Adding Reference Signal
262(1)
6.3.3 Observer Design and Implementation
262(2)
6.3.4 MATLAB Tutorial for Control Implementation
264(4)
6.3.5 Food for Thought
268(1)
6.4 Practical Issues
269(14)
6.4.1 Reducing Overshoot in Reference Tracking
269(3)
6.4.2 Anti-windup Implementation
272(4)
6.4.3 Control System using NMSS Realization
276(6)
6.4.4 Food for Thought
282(1)
6.5 Repetitive Control
283(12)
6.5.1 Basic Ideas about Repetitive Control
283(2)
6.5.2 Determining the Disturbance Model D(z)
285(5)
6.5.3 Robotic Arm Control
290(5)
6.5.4 Food for Thought
295(1)
6.6 Summary
295(1)
6.7 Further Reading
296(13)
Problems
296(13)
Part III Kalman Filtering
309(90)
7 The Kalman Filter
311(66)
7.1 Introduction
311(1)
7.2 The Kalman Filter Algorithm
312(19)
7.2.1 State Space Models in the Kalman Filter
312(1)
7.2.2 An Intuitive Computational Procedure
313(2)
7.2.3 Optimization of Kalman Filter Gain
315(2)
7.2.4 Kalman Filter Examples with MATLAB Tutorials
317(8)
7.2.5 Compensation of Sensor Bias and Load Disturbance
325(5)
7.2.6 Food for Thought
330(1)
7.3 The Kalman Filter in Multi-rate Sampling Environment
331(13)
7.3.1 KF Algorithm for Missing Data Scenarios
331(2)
7.3.2 Case Studies with MATLAB Tutorial
333(11)
7.3.3 Food for Thought
344(1)
7.4 The Extended Kalman Filter (EKF)
344(15)
7.4.1 Linearization in Extended Kalman Filter
344(4)
7.4.2 The Extended Kalman Filter Algorithm
348(3)
7.4.3 Case Studies with MATLAB Tutorial
351(8)
7.4.4 Food for Thought
359(1)
7.5 The Kalman Filter with Fading Memory
359(5)
7.5.1 The Algorithm for KF with Fading Memory
360(3)
7.5.2 Food for Thought
363(1)
7.6 Relationship between Kalman Filter and Observer
364(7)
7.6.1 One-step Kalman Filter Algorithm
364(1)
7.6.2 Kalman Filter and Observer
365(5)
7.6.3 Food for Thought
370(1)
7.7 Summary
371(1)
7.8 Further Reading
372(5)
Problems
372(5)
8 Addressing Computational Issues in KF
377(22)
8.1 Introduction
377(1)
8.2 The Sequential Kalman Filter
377(11)
8.2.1 Basic Ideas about Sequential Kalman Filter
377(5)
8.2.2 Non-diagonal R
382(1)
8.2.3 MATLAB Tutorial for Sequential Kalman Filter
383(4)
8.2.4 Food for Thought
387(1)
8.3 The Kalman Filter using UDUT Factorization
388(10)
8.3.1 Gram-Schmidt Orthogonalization Procedure
388(2)
8.3.2 Basic Ideas
390(3)
8.3.3 Sequential Kalman Filter with UDUT Decomposition
393(2)
8.3.4 MATLAB Tutorial
395(3)
8.3.5 Food for Thought
398(1)
8.4 Summary
398(1)
8.5 Further Reading
399(1)
Problems 399(4)
Bibliography 403(10)
Index 413
Liuping Wang, PhD, is a Professor of Control Engineering at RMIT University, Australia. She obtained her PhD from the Department of Control Engineering at the University of Sheffield, UK. 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 five books in systems and control.

Robin Ping Guan, PhD, obtained his Masters in Electrical Engineering from the University of Melbourne, Australia, in 2014 and his PhD from RMIT University, Australia in 2019. He is currently a research fellow in RMIT University.