Contents
. -- Preface
. -- Authors
. -- 1. Chapter 1: Introduction To Networked Control Systems
. -- 1.1. Overview Of Networked Control Techniques
. -- 1.2. Challenge In Networked Control Systems
. -- 1.2.1. Network Imperfection
. -- 1.2.1.1. Network-Induced delay
. -- 1.2.1.2. Packet Dropouts
. -- 1.2.2. Quantization
. -- 1.2.3. Network Protocol Effects
. -- 1.3. Current Research
. -- 1.3.1. Energy Efficiency
. -- 1.3.2. Spectrum Management
. -- 1.3.3. Game Theory
. -- 1.3.4. Optimal Control
. -- 1.3.5. Event- Sampled Control
. -- References
. -- 2. Chapter 2: Background On Lyapunov Stability And Stochastic Optimal Control
. -- 2.1. Deterministic Dynamical Systems
. -- 2.1.1. Discrete-Time Systems
. -- 2.1.2. Brunovsky Canonical Form
. -- 2.1.3. Linear Systems
. -- 2.1.3.1. Analysis
. -- 2.1.3.2. Simulation
. -- 2.2. Mathematical Background
. -- 2.2.1. Vector And Matrix Norms
. -- 2.2.1.1. Singular Value Decomposition
. -- 2.2.1.2. Quadratic Forms And Definiteness
. -- 2.2.2. Continuity And Function Norms
. -- 2.3. Properties Of Dynamical Systems
. -- 2.3.1. Asymptotic Stability
. -- 2.3.2. Lyapunov Stability
. -- 2.3.3. Boundedness
. -- 2.3.4. a Note On Autonomous Systems And Linear Systems
. -- 2.4. Nonlinear Stability Analysis And Control Design
. -- 2.4.1. Lyapunov Analysis For Autonomous Systems
. -- 2.4.2. Controller Design Using Lyapunov Techniques
. -- 2.4.2.1. Lyapunov Analysis And Controls Design For Linear Systems
. -- 2.4.2.2. Stability Analysis 2.4.2.3. Lyapunov Design Of Lti Feedback Controllers 2.4.3. Lyapunov Analysis For Nonautonomous Systems 2.4.4. Extensions Of Lyapunov Techniques And Bounded Stability 2.4.4.1. Uub Analysis And Controls Design 2.5. Stochastic Discrete-Time Control 2.5.1. Stochastic Lyapunov Stability 2.5.1.1. Asymptotic Stable In The Mean Square 2.5.1.2. Lyapunov Stable In The Mean Square 2.5.1.3. Bounded In The Mean Square 2.5.1.4. Bounded In The Mean 2.5.2. Stochastic Linear Discrete-Time Optimal Control 2.5.3. Stochastic Q-Learning 2.5.3.1. Q-Function Setup 2.5.3.2. Model-Free Online Tuning Based On Adaptive Estimator And Q-Learning 2.5.4. Stochastic Nonlinear Discrete-Time Optimal Control 2.5.5. Background On Neural Networks 2.5.6. Two-Layer Neural Networks 2.5.7. Nn Function Approximation 2.5.7.1. Functional Link Neural Networks Problems References 3. Chapter 3: Optimal Adaptive Control Of Uncertain Linear Network Control Systems 3.1. Traditional Control Design And Stochastic Riccati Equation-Based Solution 3.2. Finite-Horizon Optimal Adaptive Control. 3.2.1. Background 3.2.2. Stochastic Value Function 3.2.3. Model-Free Online Tuning Of Adaptive Estimator 3.2.4. Closed-Loop System Stability 3.2.5. Simulation Results 3.2.5.1. Lncs State Regulation Error And Performance 3.2.5.2. Bellman Equation And Terminal Constraint Errors 3.2.5.3. Optimality Analysis Of The Proposed Scheme 3.3. Extensions To Infinite Horizon 3.3.1. Adaptive Estimation For Optimal Regulator Design 3.3.2. Simulation Results 3.4. Conclusions Problems Appendix 3A Appendix 3B Appendix 3C Appendix 3D References 4. Chapter 4: Optimal Control Of Unknown Quantized Network Control Systems 4.1. Background 4.1.1. Quantized Linear Networked Control Systems 4.1.2. Quantizer Representation 4.1.3. Quantized Nonlinear Networked Control System 4.2. Finite-Horizon Optimal Control Of Linear Oncs 4.2.1. Action-Dependent Value-Function Setup 4.2.2. Model-Free Online Tuning Of Action-Dependent Value Function With Quantized Signals 4.2.3. Estimation Of The Optimal Feedback Control
. -- 4.2.4. Convergence Analysis 4.2.5. Simulation Results 4.3. Finite-Horizon Optimal Control Of Nonlinear Qncs 4.3.1. Observer Design 4.3.2. Near-Optimal Regulator Design 4.3.2.1. Value Function Approximation 4.3.2.2. Control Input Approximation 4.3.2.3. Dynamic Quantizer Design 4.3.2.4. Stability Analysis 4.3.2.5. Simulation Results 4.4. Conclusions Problems Appendix 4A Appendix 4B Appendix 4C Appendix 4D References 5. Chapter 5: Optimal Control Of Uncertain Linear Networked Control Systems In Input–Output Form With Disturbance Inputs 5.1. Traditional Two-Player Zero-Sum Game Design And Game-Theoretic Riccati Equation-Based Solution 5.2. Infinite-Horizon Optimal Adaptive Design 5.2.1. Background 5.2.1.1. Lncs Quadratic Zero-Sum Games 5.2.1.2. Lncs Quadratic Zero-Sum Games In Input-Output Form 5.2.2. Stochastic Value Function 5.2.3. Model-Free Online Tuning 5.2.4. Closed-Loop System Stability 5.2.5. Simulation Results 5.3. Conclusions Problems Appendix 5A Appendix 5B Appendix 5C References 6. Chapter 6: Optimal Control Of Uncertain Nonlinear Networked Control Systems Via Neurodynamic Programming 6.1. Traditional Nonlinear Optimal Control Design And Hjb Equation-Based Solution 6.2. Finite-Horizon Optimal Control For Nncs 6.2.1. Background 6.2.2. Online Nn Identifier Design. 6.2.3. Stochastic Value Function Setup And Critic Nn Design 6.2.4. Actor Nn Estimation Of Optimal Control Policy 6.2.5. Closed-Loop Stability 6.2.6. Simulation Results 6.2.6.1. State Regulation Error And Controller Performance 6.2.6.2. Hjb Equation And Terminal Constraint Estimation Errors 6.2.6.3. Cost Function Comparison 6.3. Extensions To Infinite Horizon 6.3.1. Optimal Stochastic Value Function Approximation And Control Policy Design 6.3.2. Simulation Results 6.4. Conclusions Problems
. -- References 7. Chapter 7: Optimal Design For Nonlinear Two-Player Zero-Sum Games Under Communication Constraints 7.1. Traditional Stochastic Optimal Control Design For Two-Player Zero-Sum Game 7.2. Nncs Two-Player Zero-Sum Game 7.3. Finite-Horizon Optimal Adaptive Design 7.3.1. Online Nn Identifier Design 7.3.2. Stochastic Value Function 7.3.3. Approximation Of Optimal Control And Disturbance 7.3.4. Closed-Loop System Stability 7.4. Simulation Results 7.4.1. State Regulation And Control And Disturbance Input Performance 7.4.2. Hamilton-Jacobi-Isaacs And Terminal Constraint Errors 7.4.3. Optimal Performance Of The Proposed Design 7.5. Conclusions Problems Appendix 7A Appendix 7B References 8. Chapter 8: Distributed Joint Optimal Network Scheduling And Controller Design For Wireless Networked Control Systems 8.1. Background Of Wireless Networked Control Systems 8.2. Wireless Networked Control Systems Codesign 8.2.1. Overview 8.2.2. Plant Model 8.2.3. Stochastic Optimal Control Design 8.2.4. Optimal Cross-Layer Distributed Scheduling Scheme 8.2.5. Numerical Simulations 8.3. Conclusions Problems References 9. Chapter 9: Event-Sampled Distributed Networked Control Systems 9.1. Distributed Networked Control Systems 9.2. Optimal Adaptive Event-Sampled Control 9.2.1. Zoh-Based Event-Triggered Control System 9.2.2. Optimal Adaptive Zoh-Based Event-Triggered Control 9.2.2.1. Value Function Setup 9.2.2.2. Model-Free Online Tuning Of Value Function 9.2.3. Cross-Layer Distributed Scheduling Design 9.2.3.1. Cross-Layer Design 9.2.3.2. Distributed Scheduling 9.3. Simulation 9.4. Problems References 10. Chapter 10: Optimal Control Of Uncertain Linear Control Systems Under a Unified Communication Protocol 10.1. Optimal Control Design Under Unified Communication Protocol Framework 10.1.1. Observer Design 10.1.2. Stochastic Value Function 10.1.3. Model-Free Online Tuning Of Adaptive Estimator 10.2. Closed-Loop System Stability
. -- 10.3. Simulation Results 10.3.1. Traditional Pole Placement Controller 10.3.2. Performance With Network Imperfections 10.3.3. Ncs Under Tcp With Intermittent Acknowledgment 10.3.4. Ncs Under Tcp With Full Acknowledgment 10.3.5. Ncs Under Tcp With no Acknowledgment 10.4. Conclusions Problems Appendix 10A Appendix 10B Appendix 10C References Index