Stochastic Processes, Estimation, and Control
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
Product details
November 2008Paperback
9780898716559
400 pages
254 × 177 × 19 mm
0.71kg
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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.