Synopses & Reviews
Networked control systems are increasingly ubiquitous today, with applications ranging from vehicle communication and adaptive power grids to space exploration and economics. The optimal design of such systems presents major challenges, requiring tools from various disciplines within applied mathematics such as decentralized control, stochastic control, information theory, and quantization. A thorough, self-contained book, Stochastic Networked Control Systems: Stabilization and Optimization under Information Constraints aims to connect these diverse disciplines with precision and rigor, while conveying design guidelines to controller architects. Unique in the literature, it lays a comprehensive theoretical foundation for the study of networked control systems, and introduces an array of concrete tools for work in the field.
This book lays a comprehensive theoretical foundation for the study of networked control systems, and introduces tools for work in the field. Covers characterization, comparison and design of information structures in static and dynamic teams and much more.
Table of Contents
Introduction.- Part I Information Structures in Networked Control.- Networked Control Systems as Stochastic Team Decision Problems: A General Introduction.- Characterization and Comparison of Information Structures.- Topological Properties of Information Structures: Comparison, Convergence and Optimization.- Part II Stabilization of Networked Control Systems.- Coding for Control and Connections with Information Theory.- Stochastic Stability and Drift Criteria for Markov Chains in Networked Control.- Stochastic Stabilization over Noiseless Channels.- Stochastic Stabilization over Noisy Channels.- Stabilization of Decentralized Systems over Communication Channels.- Part III Optimization in Networked Control: Design of Optimal Policies under Information Constraints.- Optimization of Real-Time Coding and Control Policies: Structural and Existence Results.- Optimal Coding and Control for Linear Gaussian Systems over Gaussian Channels under Quadratic Cost.- Agreement in Teams and the Dynamic Programming Approach under Information Constraints.- A Topological Notions and Optimization.- B Probability Theory and Stochastic Processes.- C Markov Chains, Martingales and Ergodic Processes.- D Markov Decision Theory and Optimality of Markov Policies.- References.- Index.