By Topic

Optimization Strategies in Adaptive Control: A Selective Survey

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

1 Author(s)
Jarvis, R.A. ; Australian National University, Canberra, Australia.

A great number of techniques have been applied to the general problem of adaptive control. What began as a study of engineering adaptive control problems involving dynamics, system and measurement noise, monitoring, transduction, and on-line instrumentation seems to have moved towards learning theory and methodology research that uses a refined plant/environment model as a vehicle of demonstration. An attempt is made to bring together, order, and briefly discuss many contributions in this field, bridging the era of earlier engineering practice to more recent artificial intelligence speculation. Both unimodal and multimodal strategies are discussed, together with problems arising in nonstationary environmental situations where information conservation, update, and retrieval are of considerable importance. Methods discussed include gradient, correlation, random, stochastic automata, fuzzy automata, pattern recognition, and mixed strategies. A selected reference list is provided.

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:SMC-5 ,  Issue: 1 )