By Topic

Adaptive control of black-box nonlinear systems using recurrent 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
$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)
Li Mingzhong ; Dept. of Autom. Control, Northeastern Univ., Liaoning, China ; Wang Fuli

An adaptive control method of black-box nonlinear systems is presented. The control law is derived based on minimizing a suitably chosen performance index, and its implementation requires only the calculation of two key quantities, i.e., the sensitivity between the controlled system input and output and the quasi-one-step-ahead predictive output of the controlled system. In the paper, the sensitivity of the plant is estimated using the recursive rectangular window least square algorithm, and the predictive output is obtained by a recurrent neural network. The simulation results show that the proposed adaptive control method can effectively control a class of unknown nonlinear systems

Published in:

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on  (Volume:5 )

Date of Conference:

10-12 Dec 1997