By Topic

On-line learning optimal control using successive approximation techniques

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)
Levine, M. ; McGill University, Montreal, PQ, Canada ; Vilis, T.

The application of learning theory to on-line optimization of unknown or poorly defined plants is discussed. An on-line optimization procedure is achieved by means of a learning algorithm which alters a trainable controller on the basis of an instantaneous performance criterion or subgoal. The subgoal is related to the over-all goal, the integral cost, by means of successive approximations to the Hamilton-Jacobi equation. The resulting piecewise linear controller is implemented by means of an encoder consisting of threshold logic units and a classifier consisting of a set of logic switching functions. The classifier is determined by means of an algorithm developed by Arkadev and Braverman. Features of the learning algorithm are illustrated by minimum-time and minimum-time-fuel problems.

Published in:

Automatic Control, IEEE Transactions on  (Volume:18 ,  Issue: 3 )