LINEAR STOCHASTIC CONTROL SYSTEMS Authors: Guanrong Chen (University of Houston) Goong Chen (Texas A&M University) Shih-Hsun Hsu (National Taiwan University) Publisher: CRC Press 2000 Corporate Blvd., N.W. Boca Raton, FL 33431-9868, U.S.A. Tel: 1-800-272-7737 or 407-994-0555 Fax: 1-800-374-3401 or 407-994-3625 Catalog No. 8075NHR; 1995, ISBN: 0-8493-8075-8 c. 464pp. Approx. In US - $59.95; Outside US - $72.00. This textbook is essentially self-contained; the basic prerequisites are advanced calculus, elementary ordinary differential equations, and linear algebra. The book constitutes an outgrowth of the authors' instructional material that has been developed over a 5-year period from a course in statistical control systems design which the first author has taught at the University of Houston, and a period of 10 years from courses in stochastic control systems which the second author taught at Pennsylvania State University and Texas A&M University. Over the years of teaching, we have begun to feel a need and an urge to develop a textbook that fits the instructional demands and diversity of our own students, and is accessible to general audience. Our students have come from mixed backgrounds and their interests vary from engineering to physics, applied mathematics, economics and operations research. They are generally second year graduate or higher, who have taken advanced calculus and have been exposed to some elementary statistics and deterministic control theory. As instructors, we have tried to provide guidance for students on how best to study stochastic control systems in a modern context, supplying detailed derivations and rigorous proofs, and assisting them in developing a coherent mathematical theory. It is these endeavors and our firm commitment to providing students with a strong mathematical foundation that have resulted in this modest textbook, which we hope can serve for the same purpose in graduate schools and related discipline areas. A guided plan for using this book is provided at the beginning of the text. While mathematical rigor and coherence were our main concerns in the writing of the book, the real driving force is our desire to convey those aspects of modern stochastic control theory that have actually been put to use in practical engineering applications. Every chapter contains many examples and exercises. Table of Contents Chapter 1 Introduction (including: Text organization and reading suggestion) Chapter 2 Probability and Random Processes (including: Probability theory, stochastic processes and mean-square calculus) Chapter 3 Ito Integrals and Stochastic Differential Equations (including: Integrals of orthogonal increments processes, white noise and sample calculus, Ito and Stratonovich integrals, and solutions of scalar- and vector-valued linear stochastic differential equations) Chapter 4 Analysis of Discrete-Time Linear Stochastic Control Systems (including: Analysis of causal LTI stochastic control systems, controlled Markov chains, state space systems and ARMA models, and mathematical modeling with applications) Chapter 5 Optimal Estimation for Discrete-Time Linear Stochastic Systems (including: Optimal state estimation, Kalman Filtering, numerical examples, and various modified (extended) Kalman filtering) Chapter 6 Optimal Control of Discrete-Time Linear Stochastic Systems (including: Deterministic dynamic programming and LQG optimal control problems, stochastic dynamic programming and the separation principle, adaptive stochastic control, system parameter identification and system prediction of ARMA models) Chapter 7 Continuous-Time Linear Stochastic Control Systems (including: Analysis of continuous-time causal LTI systems, Markov diffusion processes, deterministic dynamic programming and LQ optimal control problems) Chapter 8 Optimal Control of Continuous-Time Linear Stochastic Systems (including: Continuous-time LQ stochastic control problem, stochastic dynamic programming, Kalman-Bucy filtering, optimal prediction and smoothing, and the separation principle) Chapter 9 Stability Analysis of Stochastic Differential Equations (including: Stability of deterministic and stochastic systems) Chapter 10 Appendix 10.1 Fundamental Real and Functional Analysis 10.2 Fundamental Matrix Theory and Vector Calculations 10.3 Martingales References/Index |
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