By Topic

Adaptive output feedback control of nonlinear systems using dynamic 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

3 Author(s)
Yugang Niu ; Sch. of Inf., East China Univ. of Sci. & Technol., Shanghai, China ; Xingyu Wang ; Chen Hu

In this paper, a dynamic-neural-networks-based adaptive output feedback controller for a class of unknown nonlinear systems is developed under the constraint that only the system output is available for measurement. A model-free state observer is utilized to estimate the system states. Moreover, the effect of network modeling error is also discussed. By means of a Lyapunov method and a matrix Riccati equation, it has been shown that the output feedback control law and weight update laws provide robust stability for the closed-loop system, and guarantee that all signals involved are bounded and the system output tracking error is uniformly ultimately bounded.

Published in:

Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on  (Volume:1 )

Date of Conference: