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Stochastic Processes, Estimation, and Control

Part of Advances in Design and Control

Authors:
Jason L. Speyer, University of California, Los Angeles
Walter H. Chung, University of California, Los Angeles

Date Published: November 2008
availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial null Mathematics for availability.
format: Paperback
isbn: 9780898716559

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Description

A comprehensive treatment of stochastic systems beginning with the foundations of probability and ending with stochastic optimal control. The book divides into three interrelated topics. First, the concepts of probability theory, random variables and stochastic processes are presented, which leads easily to expectation, conditional expectation, and discrete time estimation and the Kalman filter. With this background, stochastic calculus and continuous-time estimation are introduced. Finally, dynamic programming for both discrete-time and continuous-time systems leads to the solution of optimal stochastic control problems resulting in controllers with significant practical application. This book will be valuable to first year graduate students studying systems and control, as well as professionals in this field.
- Demonstrates how probability can be used to model uncertainty in control and estimation problems
- Explains how the solution of optimal stochastic control problems results in controllers with significant practical application
- A thorough treatment of stochastic systems from the foundations of probability to stochastic optimal control
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Product details
- Date Published: November 2008
- format: Paperback
- isbn: 9780898716559
- length: 400 pages
- dimensions: 254 x 177 x 19 mm
- weight: 0.71kg
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial null Mathematics for availability.
Table of Contents
Preface
1. Probability theory
2. Random variables and stochastic processes
3. Conditional expectations and discrete-time Kalman filtering
4. Least squares, the orthogonal projection lemma, and discrete-time Kalman filtering
5. Stochastic processes and stochastic calculus
6. Continuous-time Gauss-Markov systems: continuous-time Kalman filter, stationarity, power spectral density, and the Wiener filter
7. The extended Kalman filter
8. A selection of results from estimation theory
9. Stochastic control and the linear quadratic Gaussian control problem
10. Linear exponential Gaussian control and estimation
Bibliography
Index.
Authors
Jason L. Speyer, University of California, Los Angeles
Professor Jason L. Speyer received his B.S. in Aeronautics and Astronautics from M.I.T. (1960) and his Ph.D. in Applied Mathematics from Harvard University (1968). His industrial experience includes research at Boeing, Raytheon, Analytical Mechanics Associated, and the Charles Stark Draper Laboratory. He was the Harry H. Power Professor in Engineering Mechanics, University of Texas, Austin. Currently, he is a Distinguished Professor in the Mechanical and Aerospace Engineering Department and the Electrical Engineering Department at the University of California, Los Angeles. Professor Speyer has twice been an elected member of the Board of Governors of the IEEE Control Systems Society. He served as Associate Editor for Technical Notes and Correspondence (1975–76) and Stochastic Control (1978–79), IEEE Transactions on Automatic Control, for AIAA Journal of Spacecraft and Rockets (1976–77), AIAA Journal of Guidance and Control (1977–78), and for Journal of Optimization Theory and Applications, 1981–present. He is fellow of AIAA and IEEE (Life Fellow) and was awarded AIAA Mechanics and Control of Flight Award, AIAA Dryden Lectureship in Research, Air Force Exceptional Civilian Decoration (1991 and 2001), IEEE Third Millennium Medal, and membership in the National Academy of Engineering. In 2002 the UCLA Autonomous Vehicle System Instrumentation Laboratory under his direction was awarded the NASA Public Service Group Achievement Award for exceptional service, commitment, and dedication toward the successful development of the Dryden Flight Research Center Autonomous Flight Formation project.
Walter H. Chung, University of California, Los Angeles
Walter Chung received his B.S. in Aeronautics and Astronautics from M.I.T. (1990), his M.S. in Aeronautics and Astronautics from Stanford University (1992) and his Ph.D. in Aerospace Engineering from the University of California, Los Angeles (1997). He currently works in the aerospace industry and has taught gradaute courses in stochastic processes, estimation and control at UCLA since 1997.