By Topic

Adaptive critic motion controller based on sparse radial basis function network

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
$33 $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)
Wei-Song Lin ; Department of Electrical Engineering, National Taiwan University, Taiwan ; Chia-Hsiang Tu

Motion controllers capable of incremental learning and optimization can automatically tune their parameters to pursue optimal control. By implementing reinforcement learning and approximate dynamic programming, an adaptive critic motion controller is shown able to achieve this objective. The control policy and the adaptive critic are implemented by sparse radial basis function networks. The policy and the critic updating rules are derived. Ability and performance of the adaptive critic motion controller is demonstrated by the control of a rotary inverted pendulum system.

Published in:

2008 World Automation Congress

Date of Conference:

Sept. 28 2008-Oct. 2 2008