By Topic

One step ahead predictive control of nonlinear systems by 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)
Jianbin Hao ; ESAT Lab., Katholieke Univ., Leuven, Belgium ; Shaohua Tan ; Vandewalle, J.

Using the properties of universal approximation of multilayer perceptron neural networks, a class of discrete nonlinear dynamical systems are modeled by a perceptron with two hidden layers. The authors' backpropagation algorithm is then used to train the model to identify the nonlinear systems to a desired degree of accuracy. Based on the identified model, a one step ahead predictive control scheme is proposed in which the future control inputs are obtained through some nonlinear optimization process. Making use of the online learning properties of neural networks, the predictive control scheme is further developed into an adaptive one which is robust to the incompleteness of identification. Simulation results show that the control scheme works well even for some very complicated nonlinear systems.

Published in:

Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on  (Volume:3 )

Date of Conference:

25-29 Oct. 1993