By Topic

A method of offset error reduction of simple adaptive control using neural networks for MIMO 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
$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)
Yasser, M. ; IDS Res. Group, Hiroshima, Japan ; Mizumoto, I.

This paper proposes an SAC using neural networks with offset error reduction for MIMO nonlinear systems. In this proposed method, the control input for the nonlinear plant is given by the sum of the output of a simple adaptive controller and the output of neural networks. The role of neural networks is to compensate for constructing a linearized model so as to minimize the output error caused by nonlinearities in the control system. The neural networks use the backpropagation algorithm for the learning process. The role of simple adaptive controller is to perform the model matching for the linear system with unknown structures to a given linear reference model. In this method, only part of the control input is fed to the PFC. Thus, the proposed method will reduce the offset error, and both of the augmented plant output and the real plant output can follow significantly close to the output of the reference model. Finally, the effectiveness of this method is confirmed through computer simulations.

Published in:


Date of Conference:

18-21 Aug. 2009