Rapid advances in communication technology have created the possibility of large-scale control systems with distribution of control tasks among several processors via communication channels. Such control systems may be distributed over large distances and may use large numbers of actuators and sensors. The possibility of such networked control systems has motivated the development of a new chapter in control theory in which control and communication issues are integrated, and all the limitations of communication channels are considered.

Although there is an emerging literature on this topic, this is the first book that attempts to present a systematic theory of estimation and control over communication networks. Using several selected problems of estimation and control over communication networks, the authors present and prove a number of results concerning optimality, stability, and robustness having practical significance for networked control system design. In particular, various problems of Kalman filtering, stabilization, and optimal control over communication channels are considered and solved. The results establish fundamental links among mathematical control theory, Shannon information theory, and entropy theory of dynamical systems.

Key features and topics of the work:

Offers accessible but precise development of important mathematical models and results
Covers estimation and stabilization of both linear and nonlinear systems
Addresses limited capacity communication channels, noisy discrete channels, systems with disturbances, irregular transmission times, and switched sensors
Presents state-of-the-art developments and cutting-edge results in the field

This essentially self-contained monograph offers accessible mathematical models and results for advanced postgraduate students, researchers, and practitioners working in the areas of control engineering, communications, information theory, signal processing, and applied mathematics who have an interest in the emerging field of networked control systems.

Résumé

Although there is an emerging literature on the topic, this is the first book that attempts to present a systematic theory of estimation and control over communication networks. Using several selected problems of estimation and control over communication networks, the authors present and prove a number of results concerning optimality, stability, and robustness having practical significance for networked control system design. In particular, various problems of Kalman filtering, stabilization, and optimal control over communication channels are considered and solved. The results establish fundamental links among mathematical control theory, Shannon information theory, and entropy theory of dynamical systems.

Contenu
Topological Entropy, Observability, Robustness, Stabilizability, and Optimal Control.- Stabilization of Linear Multiple Sensor Systems via Limited Capacity Communication Channels.- Detectability and Output Feedback Stabilizability of Nonlinear Systems via Limited Capacity Communication Channels.- Robust Set-Valued State Estimation via Limited Capacity Communication Channels.- An Analog of Shannon Information Theory: State Estimation and Stabilization of Linear Noiseless Plants via Noisy Discrete Channels.- An Analog of Shannon Information Theory: State Estimation and Stabilization of Linear Noisy Plants via Noisy Discrete Channels.- An Analog of Shannon Information Theory: Stable in Probability Control and State Estimation of Linear Noisy Plants via Noisy Discrete Channels.- Decentralized Stabilization of Linear Systems via Limited Capacity Communication Networks.- H1 State Estimation via Communication Channels.- Kalman State Estimation and Optimal Control Based on Asynchronously and Irregularly Delayed Measurements.- Optimal Computer Control via Asynchronous Communication Channels.- Linear-Quadratic Gaussian Optimal Control via Limited Capacity Communication Channels.- Kalman State Estimation in Networked Systems with Asynchronous Communication Channels and Switched Sensors.- Robust Kalman State Estimation with Switched Sensors.