By Topic

A direct adaptive neural-network control 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)
Niu Lin ; Kunming Univ. of Sci. & Technol., China ; Zhang Yunsheng

A direct adaptive neural-network control strategy for a class of nonlinear system is presented. The system considered is described by an unknown NARMA model and a feedforward neural network is used to learn the system. Taking the neural network as a model of the system, control signals are directly obtained by minimizing either the instant difference or the cumulative differences between a setpoint and output of the model. To accelerate learning and improve convergence the technique in generalized predictive control theory and the gradient descent rule are used in this paper. The effectiveness of the proposed control scheme is illustrated through simulations

Published in:

Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on  (Volume:5 )

Date of Conference: