By Topic

Structured wavelet-based neural network for 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)
Karami, A. ; Control & Intell. Process. Center of Excellence, Univ. of Tehran, Tehran, Iran ; Yazdanpanah, M.J.

In this paper, a wavelet-based neural network is proposed for the control of nonlinear systems. Activation functions of neural network nodes are determined based on the wavelet transform. The controller can efficiently compensate for the undesired effects of hard nonlinearities such as saturation and/or dead zone of control input. Compared with standard neuro-controllers, the structure of the controller is definite and simple. The proposed controller is localizable and has a systematically chosen structure, which improves the close-loop performance. An off-line algorithm determines the number of nodes. In addition, an on-line algorithm adjusts the parameters of wavelet bases and network weights. Back propagation algorithm with a momentum term is used for updating the weights and parameters of activation functions. This controller reduces the quantity of network parameters, calculation cost and convergence time of online algorithms with respect to the conventional neural network. Also, the controller is able to control unstable and MIMO systems. To illustrate the capability and performance superiority of the proposed controller, two nonlinear systems are controlled and the corresponding results are compared.

Published in:

Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on

Date of Conference:

12-15 Dec. 2011