This text for upper-level undergraduates and graduate students explores stochastic control theory in terms of analysis, parametric optimization, and optimal stochastic control. Limited to linear systems with quadratic criteria, it covers discrete time as well as continuous time systems.
The first three chapters provide motivation and background material on stochastic processes, followed by an analysis of dynamical systems with inputs of stochastic processes. A simple version of the problem of optimal control of stochastic systems is discussed, along with an example of an industrial application of this theory. Subsequent discussions cover filtering and prediction theory as well as the general stochastic control problem for linear systems with quadratic criteria.
Each chapter begins with the discrete time version of a problem and progresses to a more challenging continuous time version of the same problem. Prerequisites include courses in analysis and probability theory in addition to a course in dynamical systems that covers frequency response and the state-space approach for continuous time and discrete time systems.
Introduction to Stochastic Control Theory (Dover Books on Electrical Engineering)
📄 Viewing lite version
Full site ›
Book Details
Author(s)Karl J. Astrom
PublisherDover Publications
ISBN / ASIN0486445313
ISBN-139780486445311
AvailabilityUsually ships in 24 hours
Sales Rank1,204,353
CategoryTechnology & Engineering
MarketplaceUnited States 🇺🇸
Description ▲
More Books in Technology & Engineering
Fourth Dimension in Building: Strategies for Avoiding …
View
Design and Evaluation of Rigid and Flexible Pavements,…
View
Nuclear Nonproliferation: Status Of U.s. Efforts To Im…
View
Time-Domain Numerical Methods for Modelling Antennas, …
View
The Rise of the Standard Model: A History of Particle …
View
Synthesis, Properties and Crystal Chemistry of Perovsk…
View
Error Propagation in Environmental Modelling with GIS …
View
Crops And Environmental Change: An Introduction To Eff…
View
Multicarrier Modulation with Low PAR: Applications to …
View