内容简介：　　　

　　This monograph focuses on characterizing the stability and performance consequences of inserting limited-capacity communication networks within a control loop. The text shows how integration of the ideas of control and estimation with those of communication and information theory can be used to provide important insights concerning several fundamental problems such as: minimum data rate for stabilization of linear systems over noisy channels; minimum network requirement for stabilization of linear systems over fading channels; and stability of Kalman filtering with intermittent observations.

　　A fundamental link is revealed between the topological entropy of linear dynamical systems and the capacities of communication channels. The design of a logarithmic quantizer for the stabilization of linear systems under various network environments is also extensively discussed and solutions to many problems of Kalman filtering with intermittent observations are demonstrated.

　　Analysis and Design of Networked Control Systems will interest control theorists and engineers working with networked systems and may also be used as a resource for graduate students with backgrounds in applied mathematics, communications or control who are studying such systems.

英文目录：
Preface
1 Overview of Networked Control Systems
　　1.1 Introduction and Motivation
　　　　1.1.1 Components of NCS
　　　　1.1.2 Brief History of NCS
　　　　1.1.3 Challenges in NCS
　　1.2 Preview of the Book
　　References
2 Entropies and Capacities in Networked Control Systems
　　2.1 Entropies
　　　　2.1.1 Entropy in Information Theory
　　　　2.1.2 Topological Entropy in Feedback Theory
　　2.2 Channel Capacities
　　　　2.2.1 Noiseless Channels
　　　　2.2.2 Noisy Channels
　　2.3 Control Over Communication Networks
　　　　2.3.1 Quantized Control Over Noiseless Networks
　　　　2.3.2 Quantized Control Over Noisy Networks
　　2.4 Estimation Over Communication Networks
　　　　2.4.1 Quantized Estimation Over Noiseless Networks
　　　　2.4.2 Data-Driven Communication for Estimation
　　　　2.4.3 Estimation Over Noisy Networks
　　2.5 Open Problems
　　References
3 Data Rate Theorem for Stabilization Over Noiseless Channels
　　3.1 Problem Statement
　　3.2 Classical Approach for Quantized Control
　　3.3 Data Rate Theorem for Stabilization
　　　　3.3.1 Proof of Necessity
　　　　3.3.2 Proof of Sufficiency
　　3.4 Summary
　　References
4 Data Rate Theorem for Stabilization Over Erasure Channels
　　4.1 Problem Formulation
　　4.2 Single Input Case
　　　　4.2.1 Proof of Necessity
　　　　4.2.2 Proof of Sufficiency
　　4.3 Multiple Input Case
　　4.4 Summary
　　References
5 Data Rate Theorem for Stabilization Over Gilbert-Elliott Channels
　　5.1 Problem Formulation
　　5.2 Preliminaries
　　　　5.2.1 Random Down Sampling
　　　　5.2.2 Statistical Properties of Sojourn Times
　　5.3 Scalar Systems
　　　　5.3.1 Noise Free Systems with Bounded Initial Support
　　　　5.3.2 Proof of Necessity
　　　　5.3.3 Proof of Sufficiency
　　5.4 General Stochastic Scalar Systems
　　　　5.4.1 Proof of Necessity
　　　　5.4.2 Proof of Sufficiency
　　5.5 Vector Systems
　　　　5.5.1 Real Jordan Form
　　　　5.5.2 Necessity
　　　　5.5.3 Sufficiency
　　　　5.5.4 An Example
　　5.6 Summary
　　References
6 Stabilization of Linear Systems Over Fading Channels
　　6.1 Problem Formulation
　　6.2 State Feedback Case
　　　　6.2.1 Parallel Transmission Strategy
　　　　6.2.2 Serial Transmission Strategy
　　6.3 Output Feedback Case
　　　　6.3.1 SISO Plants
　　　　6.3.2 Triangularly Decoupled Plants
　　6.4 Extension and Application
　　　　6.4.1 Stabilization Over Output Fading Channels
　　　　6.4.2 Stabilization of a Finite Platoon
　　6.5 Channel Processing and Channel Feedback
　　6.6 Power Constraint
　　　　6.6.1 Feedback Stabilization
　　　　6.6.2 Performance Design
　　　　6.6.3 Numerical Example
　　6.7 Summary
　　References
7 Stabilization of Linear Systems via Infinite-Level Logarithmic Quantization
　　7.1 State Feedback Case
　　　　7.1.1 Logarithmic Quantization
　　　　7.1.2 Sector Bound Approach
　　7.2 Output Feedback Case
　　　　7.2.1 Quantized Control
　　　　7.2.2 Quantized Measurements
　　7.3 Stabilization of MIMO Systems
　　　　7.3.1 Quantized Control
　　　　7.3.2 Quantized Measurements
　　7.4 Quantized Quadratic Performance Control
　　7.5 Quantized H1 Control
　　7.6 Summary
　　References
8 Stabilization of Linear Systems via Finite-Level Logarithmic Quantization
　　8.1 Quadratic Stabilization via Finite-level Quantization
　　　　8.1.1 Finite-level Quantizer
　　　　8.1.2 Number of Quantization Levels
　　　　8.1.3 Robustness Against Additive Noises
　　　　8.1.4 Illustrative Examples
　　8.2 Attainability of the Minimum Data Rate for Stabilization
　　　　8.2.1 Problem Simplification
　　　　8.2.2 Network Configuration
　　　　8.2.3 Quantized Control Feedback
　　　　8.2.4 Quantized State Feedback
　　8.3 Summary
　　References
9 Stabilization of Markov Jump Linear Systems via Logarithmic Quantization
　　9.1 State Feedback Case
　　　　9.1.1 Feedback Stabilization
　　　　9.1.2 Special Schemes
　　　　9.1.3 Mode Estimation
　　9.2 Stabilization Over Lossy Channels
　　　　9.2.1 Binary Dropouts Model
　　　　9.2.2 Bounded Dropouts Model
　　　　9.2.3 Extension to Output Feedback
　　9.3 Summary
　　References
10 Kalman Filtering with Quantized Innovations
　　10.1 Problem Formulation
　　10.2 Quantized Innovations Kalman Filter
　　　　10.2.1 Multi-level Quantized Filtering
　　　　10.2.2 Optimal Quantization Thresholds
　　　　10.2.3 Convergence Analysis
　　10.3 Robust Quantization
　　10.4 A Numerical Example
　　10.5 Summary
　　References
11 LQG Control with Quantized Innovation Kalman Filter
　　11.1 Problem Formulation
　　11.2 Separation Principle
　　11.3 State Estimator Design
　　11.4 Controller Design
　　11.5 An Illustrative Example
　　11.6 Summary
　　References
12 Kalman Filtering with Faded Measurements
　　12.1 Problem Formulation
　　12.2 Stability Analysis of Kalman Filter with Fading
　　　　12.2.1 Preliminaries
　　　　12.2.2 Mean Covariance Stability
　　12.3 A Numerical Example
　　12.4 Summary
　　References
13 Kalman Filtering with Packet Losses
　　13.1 Networked Estimation
　　　　13.1.1 Intermittent Kalman Filter
　　　　13.1.2 Stability Notions
　　13.2 Equivalence of the Two Stability Notions
　　13.3 Second-Order Systems
　　13.4 Higher-Order Systems
　　　　13.4.1 Non-degenerate Systems
　　13.5 Illustrative Examples
　　13.6 Proofs
　　　　13.6.1 Proof of Theorem 13.3
　　　　13.6.2 Proof of Theorem 13.4
　　　　13.6.3 Proofs of Results in Sect. 13.4
　　13.7 Summary
　　References
14 Kalman Filtering with Scheduled Measurements
　　14.1 Networked Estimation
　　　　14.1.1 Scheduling Problems
　　14.2 Controllable Scheduler
　　　　14.2.1 An Approximate MMSE Estimator
　　　　14.2.2 An Illustrative Example
　　　　14.2.3 Stability Analysis
　　14.3 Uncontrollable Scheduler
　　　　14.3.1 Intermittent Kalman Filter
　　　　14.3.2 Second-Order System
　　　　14.3.3 Higher-Order System
　　14.4 Summary
　　References
15 Parameter Estimation with Scheduled Measurements
　　15.1 Innovation Based Scheduler
　　15.2 Maximum Likelihood Estimation
　　　　15.2.1 ML Estimator
　　　　15.2.2 Estimation Performance
　　　　15.2.3 Optimal Scheduler
　　15.3 Naive Estimation
　　15.4 Iterative ML Estimation
　　　　15.4.1 Adaptive Scheduler
　　15.5 Proof of Theorem 15.1
　　15.6 EM-Based Estimation
　　　　15.6.1 Design of b yk
　　15.7 Numerical Example
　　15.8 Summary
　　References
Appendix A: On Matrices
Index
A Background Materials
　　A.1 Martingales
　　A.2 Markov Chains
　　A.3 Weak Convergence
　　A.4 Miscellany
References
Index