By Topic

Adaptive control design using delayed dynamical neural networks 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
$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)
Wen-Shyong Yu ; Dept. of Electr. Eng., Tatung Univ., Taipei, Taiwan ; Gwo-Chuan Wang

In this paper, an adaptive control algorithm via delayed dynamical neural nets (DDNNs) for a class of nonlinear systems is presented. We identify the nonlinear system by updating the weights of the DDNNs and then design the controller adaptively based on the neural networks model to achieve the model following purpose. An analysis via Lyapunov stability criteria shows that the proposed control algorithm guarantees parameter estimation convergence and system stability, with the output of the system following the specified reference model. Finally, a series of simulations are performed to demonstrate the effectiveness of the proposed scheme.

Published in:

Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on  (Volume:4 )

Date of Conference:

2001