By Topic

Robust Adaptive Neural Network Control for a Class of Nonlinear Systems

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

3 Author(s)
Yisha Liu ; Dalian University of Technology, China ; Wei Wang ; Yanjun Liu

In this paper, a stable robust adaptive control approach is presented for a class of unknown nonlinear systems in the strict-feedback form with disturbances. The key assumption is that neural network approximation errors and external disturbances satisfy certain bounding conditions. By combining neural network technique with backstepping method and introducing a special type of Lyapunov functions, the controller singularity problem is avoided perfectly. As the estimates of unknown neural network approximation error bound and external disturbances bound are adjusted adaptively, the robustness of the closed-loop system is improved and the application scope of nonlinear systems is extended. The overall neural network control systems can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded and the tracking error converges to a small neighborhood of zero by suitably choosing the design parameters. The feasibility of the control approach is demonstrated through simulation results

Published in:

Sixth International Conference on Intelligent Systems Design and Applications  (Volume:1 )

Date of Conference:

16-18 Oct. 2006