Cart (Loading....) | Create Account
Close category search window
 

A new approach to stochastic adaptive control

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Meyn, S.P. ; McGill University, Montr??, Canada ; Caines, P.E.

The principal techniques used up to now for the analysis of stochastic adaptive control systems have been (i) super-martingale (often called stochastic Lyapunov) methods and (ii) methods relying upon the strong consistency of some parameter estimation scheme. Optimal stochastic control and filtering methods have also been employed. Although there have been some successes, the extension of these techniques to a broad class of adptive control problems, including the case of time varying parameters, has been difficult. In this paper a new approach is adopted: If an underlying Markovian state space system for the controlled process is available, and if this process possesses stationary transition probabilities, then the powerful ergodic theory of Markov processes may be applied. Subject to technical conditions one may deduce (amongst other facts) (i) the existence of an invariant measure ???? for the process and (ii) the convergence almost surely of the sample averages of a function of the state process (and of its expectation) to its conditional expectation [????] with respect to a sub-??-field of invariant sets ??I. The technique is illustrated by an application to a previously unsolved problem involving a linear system with unbounded random time varying parameters. Work suppoted by Canada NSERC Grant No.: 1329 and a UK SERC Visiting Research Fellowship.

Published in:

Decision and Control, 1986 25th IEEE Conference on

Date of Conference:

10-12 Dec. 1986

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.