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

Optimal Control of a Class of Nonlinear Systems Using Radial Basis Function Neural Networks

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)
Medagam, P.V. ; Southern Illinois Univ., Carbondale ; Pourboghrat, F.

This paper presents an online optimal control technique for a class of nonlinear systems. The technique is based on approximating the solution to the corresponding generalized Hamilton-Jacobi-Bellman (GHJB) equation for optimal control using radial basis function neural networks (RBFNN). The GHJB equation is solved by adjusting the parameters (weights and centers) of RBFNN online. The proposed optimal control algorithm provides good accuracy and numerical examples illustrate the merits of the proposed approach.

Published in:

Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on  (Volume:4 )

Date of Conference:

13-15 Dec. 2007

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.