Overview

Linear Stochastic Control Systems presents a thorough description of the mathematical theory and fundamental principles of linear stochastic control systems. Both continuous-time and discrete-time systems are thoroughly covered.Reviews of the modern probability and random processes theories and the It= stochastic differential equations are provided. Discrete-time stochastic systems theory, optimal estimation and Kalman filtering, and optimal stochastic control theory are studied in detail. A modern treatment of these same topics for continuous-time stochastic control systems is included. The text is written in an easy-to-understand style, and the reader needs only to have a background of elementary real analysis and linear deterministic systems theory to comprehend the subject matter. This graduate textbook is also suitable for self-study, professional training, and as a handy research reference. Linear Stochastic Control Systems is self-contained and provides a step-by-step development of the theory, with many illustrative examples, exercises, and engineering applications.

Full Product Details

Author:   Goong Chen ,  Richard Durrett ,  Mark Pinsky (Northwestern University, Evanston, Illinois, USA) ,  Guanrong Chen (City University of Hong Kong, Kowloon)
Publisher:   Taylor & Francis Inc
Imprint:   CRC Press Inc
Volume:   3
Dimensions:   Width: 15.60cm , Height: 2.60cm , Length: 23.40cm
Weight:   0.771kg
ISBN:  

9780849380754

ISBN 10:   0849380758
Pages:   400
Publication Date:   12 July 1995
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   Out of stock  

Table of Contents

Preface Introduction From Deterministic to Stochastic Linear Control Systems Text Organization and Reading Suggestion MATHEMATICAL PRELIMINARIES Probability and Random Processes Probability, Measure, and Integration Convergence of Random Sequences Random Vectors and Conditional Expectations Second Order Processes and Calculus in Mean Square Exercises References Ito Integrals and Stochastic Differential Equations Markov Processes Orthogonal Increments Processes and the Wiener-Levy Process Ito Integrals and Stochastic Differential Equations Exercises References LINEAR STOCHASTIC CONTROL SYSTEMS: THE DISCRETE-TIME CASE Analysis of Discrete-Time Linear Stochastic Control Systems Analysis of Discrete-Time Causal LTI Systems Analysis of Causal LTI Stochastic Control Systems Analysis of the State Description of Controlled Markov Chains State Space Systems and ARMA Models Mathematical Modeling and Applications Exercises References Optimal Estimation for Discrete-Time Linear Stochastic Systems Optimal State Estimation Recursive Optimal Estimation and Kalman Filtering Modified Kalman Filtering Algorithms Exercises References Optimal Control of Discrete-Time Linear Stochastic Systems Introduction Dynamic Programming and LQC Control Problems LQC Optimal Control Problems Adaptive Stochastic Control Exercises References LINEAR STOCHASTIC CONTROL SYSTEMS: THE CONTINUOUS-TIME CASE Continuous-Time Linear Stochastic Control Systems Analysis of Continuous-Time Causal LTI Systems Further Discussion of Markov Processes Dynamic Programming and LQ Control Problems Exercises References Optimal Control of Continuous-Time Linear Stochastic Systems The Continuous-Time LQ Stochastic Control Problem Stochastic Dynamic Programming Innovation Processes and the Kalman-Bucy Filter Optimal Prediction and Smoothing The Separation Principle

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Chen\, Goong; Chen\, Guanrong; Hsu\, Shih-Hsun

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