This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.
Audience: This book is recommended for those who have been introduced to probability theory and stochastic processes and want to learn more about decision making under uncertainty. It can be used as a one- or two-semester course textbook for advanced undergrad or first-year graduate students.
Contents: Chapter 1: Introduction; Chapter 2: State space models; Chapter 3: Properties of linear stochastic systems; Chapter 4: Controlled Markov chain model; Chapter 5: Input output models; Chapter 6: Dynamic programming; Chapter 7: Linear systems: estimation and control; Chapter 8: Infinite horizon dynamic programming; Chapter 9: Introduction to system identification; Chapter 10: Linear system identification; Chapter 11: Bayesian adaptive control; Chapter 12: Non-Bayesian adaptive control; Chapter 13: Self-tuning regulators for linear systems
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Pravin Varaiya is a Professor of the Graduate School in the Department of Electrical Engineering and Computer Sciences at University of California, Berkeley. His current research focuses on transportation networks and electric power systems. He is a Fellow of IEEE and the American Academy of Arts and Sciences, and a member of the US National Academy of Engineering. He is on the editorial board of Transportation Letters and has co-authored four books, most recently, Dynamics and Control of Trajectory Tubes, Birkhäuser (2014).
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