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E-raamat: Optimal Networked Control Systems with MATLAB

(Texas A&M UniversityCorpus Christi, USA), (Missouri University of Science and Technology, Rolla, USA)
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Optimal Networked Control Systems with MATLAB® discusses optimal controller design in discrete time for networked control systems (NCS). The authors apply several powerful modern control techniques in discrete time to the design of intelligent controllers for such NCS. Detailed derivations, rigorous stability proofs, computer simulation examples, and downloadable MATLAB® codes are included for each case.

The book begins by providing background on NCS, networked imperfections, dynamical systems, stability theory, and stochastic optimal adaptive controllers in discrete time for linear and nonlinear systems. It lays the foundation for reinforcement learning-based optimal adaptive controller use for finite and infinite horizons. The text then:











Introduces quantization effects for linear and nonlinear NCS, describing the design of stochastic adaptive controllers for a class of linear and nonlinear systems Presents two-player zero-sum game-theoretic formulation for linear systems in inputoutput form enclosed by a communication network Addresses the stochastic optimal control of nonlinear NCS by using neuro dynamic programming Explores stochastic optimal design for nonlinear two-player zero-sum games under communication constraints Treats an event-sampled distributed NCS to minimize transmission of state and control signals within the feedback loop via the communication network Covers distributed joint optimal network scheduling and control design for wireless NCS, as well as the effect of network protocols on the wireless NCS controller design

An ideal reference for graduate students, university researchers, and practicing engineers, Optimal Networked Control Systems with MATLAB® instills a solid understanding of neural network controllers and how to build them.
Preface xv
Authors xix
Chapter 1 Introduction to Networked Control Systems 1(18)
1.1 Overview of Networked Control Techniques
2(1)
1.2 Challenges in Networked Control Systems
3(10)
1.2.1 Network Imperfections
4(1)
1.2.1.1 Network-Induced Delay
4(1)
1.2.1.2 Packet Dropouts
4(1)
1.2.2 Quantization
5(1)
1.2.3 Network Protocol Effects
6(7)
1.3 Current Research
13(2)
1.3.1 Energy Efficiency
13(1)
1.3.2 Spectrum Management
13(1)
1.3.3 Game Theory
13(1)
1.3.4 Optimal Control
14(1)
1.3.5 Event-Sampled Control
14(1)
References
15(4)
Chapter 2 Background on Lyapunov Stability and Stochastic Optimal Control 19(46)
2.1 Deterministic Dynamical Systems
19(4)
2.1.1 Discrete-Time Systems
19(1)
2.1.2 Brunovsky Canonical Form
20(1)
2.1.3 Linear Systems
21(2)
2.1.3.1 Analysis
22(1)
2.1.3.2 Simulation
22(1)
2.2 Mathematical Background
23(3)
2.2.1 Vector and Matrix Norms
23(2)
2.2.1.1 Singular Value Decomposition
24(1)
2.2.1.2 Quadratic Forms and Definiteness
25(1)
2.2.2 Continuity and Function Norms
25(1)
2.3 Properties of Dynamical Systems
26(3)
2.3.1 Asymptotic Stability
27(1)
2.3.2 Lyapunov Stability
27(1)
2.3.3 Boundedness
28(1)
2.3.4 A Note on Autonomous Systems and Linear Systems
28(1)
2.4 Nonlinear Stability Analysis and Controls Design
29(13)
2.4.1 Lyapunov Analysis for Autonomous Systems
29(4)
2.4.2 Controller Design Using Lyapunov Techniques
33(4)
2.4.2.1 Lyapunov Analysis and Controls Design for Linear Systems
35(1)
2.4.2.2 Stability Analysis
35(1)
2.4.2.3 Lyapunov Design of LTI Feedback Controllers
36(1)
2.4.3 Lyapunov Analysis for Nonautonomous Systems
37(2)
2.4.4 Extensions of Lyapunov Techniques and Bounded Stability
39(3)
2.4.4.1 UUB Analysis and Controls Design
39(3)
2.5 Stochastic Discrete-Time Control
42(19)
2.5.1 Stochastic Lyapunov Stability
42(1)
2.5.1.1 Asymptotic Stable in the Mean Square
43(1)
2.5.1.2 Lyapunov Stable in the Mean Square
43(1)
2.5.1.3 Bounded in the Mean Square
43(1)
2.5.1.4 Bounded in the Mean
43(1)
2.5.2 Stochastic Linear Discrete-Time Optimal Control
43(5)
2.5.3 Stochastic Q-Learning
48(3)
2.5.3.1 Q-Function Setup
48(2)
2.5.3.2 Model-Free Online Tuning Based on Adaptive Estimator and Q-Learning
50(1)
2.5.4 Stochastic Nonlinear Discrete-Time Optimal Control
51(3)
2.5.5 Background on Neural Networks
54(1)
2.5.6 Two-Layer Neural Networks
55(4)
2.5.7 NN Function Approximation
59(10)
2.5.7.1 Functional Link Neural Networks
60(1)
Problems
61(1)
References
62(3)
Chapter 3 Optimal Adaptive Control of Uncertain Linear Network Control Systems 65(40)
3.1 Traditional Control Design and Stochastic Riccati Equation-Based Solution
67(2)
3.2 Finite-Horizon Optimal Adaptive Control
69(19)
3.2.1 Background
69(3)
3.2.2 Stochastic Value Function
72(1)
3.2.3 Model-Free Online Tuning of Adaptive Estimator
73(5)
3.2.4 Closed-Loop System Stability
78(4)
3.2.5 Simulation Results
82(6)
3.2.5.1 LNCS State Regulation Error and Performance
83(1)
3.2.5.2 Bellman Equation and Terminal Constraint Errors
83(2)
3.2.5.3 Optimality Analysis of the Proposed Scheme
85(3)
3.3 Extensions to Infinite Horizon
88(8)
3.3.1 Adaptive Estimation for Optimal Regulator Design
88(4)
3.3.2 Simulation Results
92(4)
3.4 Conclusions
96(1)
Problems
97(1)
Appendix 3A
98(1)
Appendix 3B
98(1)
Appendix 3C
99(2)
Appendix 3D
101(1)
References
102(3)
Chapter 4 Optimal Control of Unknown Quantized Network Control Systems 105(50)
4.1 Background
108(6)
4.1.1 Quantized Linear Networked Control Systems
108(2)
4.1.2 Quantizer Representation
110(2)
4.1.3 Quantized Nonlinear Networked Control System
112(2)
4.2 Finite-Horizon Optimal Control of Linear QNCS
114(13)
4.2.1 Action-Dependent Value-Function Setup
115(2)
4.2.2 Model-Free Online Tuning of Action-Dependent Value Function with Quantized Signals
117(5)
4.2.3 Estimation of the Optimal Feedback Control
122(1)
4.2.4 Convergence Analysis
122(2)
4.2.5 Simulation Results
124(3)
4.3 Finite-Horizon Optimal Control of Nonlinear QNCS
127(14)
4.3.1 Observer Design
129(3)
4.3.2 Near-Optimal Regulator Design
132(26)
4.3.2.1 Value Function Approximation
133(2)
4.3.2.2 Control Input Approximation
135(2)
4.3.2.3 Dynamic Quantizer Design
137(1)
4.3.2.4 Stability Analysis
138(2)
4.3.2.5 Simulation Results
140(1)
4.4 Conclusions
141(2)
Problems
143(1)
Appendix 4A
144(1)
Appendix 4B
145(3)
Appendix 4C
148(1)
Appendix 4D
149(2)
References
151(4)
Chapter 5 Optimal Control of Uncertain Linear Networked Control Systems in Input-Output Form with Disturbance Inputs 155(32)
5.1 Traditional Two-Player Zero-Sum Game Design and Game Theoretic Riccati Equation-Based Solution
156(2)
5.2 Infinite-Horizon Optimal Adaptive Design
158(19)
5.2.1 Background
159(5)
5.2.1.1 LNCS Quadratic Zero-Sum Games
159(2)
5.2.1.2 LNCS Quadratic Zero-Sum Games in Input-Output Form
161(3)
5.2.2 Stochastic Value Function
164(3)
5.2.3 Model-Free Online Tuning
167(4)
5.2.4 Closed-Loop System Stability
171(2)
5.2.5 Simulation Results
173(4)
5.3 Conclusions
177(1)
Problems
178(1)
Appendix 5A
178(2)
Appendix 5B
180(3)
Appendix 5C
183(1)
References
184(3)
Chapter 6 Optimal Control of Uncertain Nonlinear Networked Control Systems via Neurodynamic Programming 187(34)
6.1 Traditional Nonlinear Optimal Control Design and HJB Equation-Based Solution
188(2)
6.2 Finite-Horizon Optimal Control for NNCS
190(19)
6.2.1 Background
191(1)
6.2.2 Online NN Identifier Design
192(3)
6.2.3 Stochastic Value Function Setup and Critic NN Design
195(3)
6.2.4 Actor NN Estimation of Optimal Control Policy
198(2)
6.2.5 Closed-Loop Stability
200(2)
6.2.6 Simulation Results
202(7)
6.2.6.1 State Regulation Error and Controller Performance
204(3)
6.2.6.2 HJB Equation and Terminal Constraint Estimation Errors
207(1)
6.2.6.3 Cost Function Comparison
207(2)
6.3 Extensions to Infinite Horizon
209(9)
6.3.1 Optimal Stochastic Value Function Approximation and Control Policy Design
210(4)
6.3.2 Simulation Results
214(4)
6.4 Conclusions
218(1)
Problems
219(1)
References
219(2)
Chapter 7 Optimal Design for Nonlinear Two-Player Zero-Sum Games under Communication Constraints 221(36)
7.1 Traditional Stochastic Optimal Control Design for Two-Player Zero-Sum Game
223(2)
7.2 NNCS Two-Player Zero-Sum Game
225(2)
7.3 Finite-Horizon Optimal Adaptive Design
227(15)
7.3.1 Online NN Identifier Design
227(3)
7.3.2 Stochastic Value Function
230(5)
7.3.3 Approximation of Optimal Control and Disturbance
235(4)
7.3.4 Closed-Loop System Stability
239(3)
7.4 Simulation Results
242(5)
7.4.1 State Regulation and Control and Disturbance Input Performance
242(2)
7.4.2 Hamilton-Jacobi-Isaacs and Terminal Constraint Errors
244(3)
7.4.3 Optimal Performance of the Proposed Design
247(1)
7.5 Conclusions
247(1)
Problems
247(1)
Appendix 7A
248(1)
Appendix 7B
249(6)
References
255(2)
Chapter 8 Distributed Joint Optimal Network Scheduling and Controller Design for Wireless Networked Control Systems 257(18)
8.1 Background of Wireless Networked Control Systems
258(1)
8.2 Wireless Networked Control Systems Codesign
259(13)
8.2.1 Overview
259(1)
8.2.2 Plant Model
260(1)
8.2.3 Stochastic Optimal Control Design
261(3)
8.2.4 Optimal Cross-Layer Distributed Scheduling Scheme
264(5)
8.2.5 Numerical Simulations
269(3)
8.3 Conclusions
272(1)
Problems
272(1)
References
272(3)
Chapter 9 Event-Sampled Distributed Networked Control Systems 275(22)
9.1 Distributed Networked Control Systems
277(2)
9.2 Optimal Adaptive Event-Sampled Control
279(12)
9.2.1 ZOH-Based Event-Triggered Control System
279(1)
9.2.2 Optimal Adaptive ZOH-Based Event-Triggered Control
280(4)
9.2.2.1 Value Function Setup
280(1)
9.2.2.2 Model-Free Online Tuning of Value Function
281(3)
9.2.3 Cross-Layer Distributed Scheduling Design
284(14)
9.2.3.1 Cross-Layer Design
284(1)
9.2.3.2 Distributed Scheduling
284(7)
9.3 Simulation
291(3)
9.4 Conclusions
294(1)
Problems
295(1)
References
295(2)
Chapter 10 Optimal Control of Uncertain Linear Control Systems under a Unified Communication Protocol 297(32)
10.1 Optimal Control Design under Unified Communication Protocol Framework
298(9)
10.1.1 Observer Design
299(3)
10.1.2 Stochastic Value Function
302(2)
10.1.3 Model-Free Online Tuning of Adaptive Estimator
304(3)
10.2 Closed-Loop System Stability
307(2)
10.3 Simulation Results
309(6)
10.3.1 Traditional Pole Placement Controller Performance with Network Imperfections
310(1)
10.3.2 NCS under TCP with Intermittent Acknowledgment
310(2)
10.3.3 NCS under TCP with Full Acknowledgment
312(1)
10.3.4 NCS under UDP with No Acknowledgment
313(2)
10.4 Conclusions
315(1)
Problems
316(1)
Appendix 10A
317(4)
Appendix 10B
321(3)
Appendix 10C
324(3)
References
327(2)
Index 329
Jagannathan Sarangapani (referred to as S. Jagannathan) is a Rutledge-Emerson endowed chair professor of electrical and computer engineering and the site director for the National Science Foundation Industry/University Cooperative Research Center on Intelligent Maintenance Systems at the Missouri University of Science and Technology, Rolla, USA (former University of Missouri-Rolla, USA). Widely published and highly decorated with 20 US patents, he is a fellow of the Institute of Measurement and Control, UK, and the Institution of Engineering and Technology, UK. He has been on the organizing committees of several IEEE conferences, and served as the IEEE Control Systems Society Intelligent Control Technical Committee chair.

Hao Xu earned his masters degree in electrical engineering from Southeast University, Nanjing, China, in 2009, and his Ph.D from the Missouri University of Science and Technology, Rolla, USA (formerly, the University of Missouri-Rolla, USA), in 2012. Currently, he is an assistant professor in the College of Science and Engineering and the director of the Unmanned Systems Research Laboratory at Texas A&M UniversityCorpus Christi, USA. His research interests include autonomous unmanned aircraft systems, multi-agent systems, wireless passive sensor networks, localization, detection, networked control systems, cyber-physical systems, distributed network protocol design, optimal control, and adaptive control.
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