By Topic

Model reference adaptive control of nonlinear dynamical systems using multilayer 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)
Jagannathan, S. ; Automation & Robotics Res. Inst., Fort Worth, TX, USA ; Lewis, F.L. ; Pastravanu, O.

A multilayer discrete-time neural net (NN) controller is presented for the model reference adaptive control of a class of MIMO dynamical systems. No initial learning phase is needed and the tracking error between the output of the nonlinear plant and a linear model converges within a very short time. This weight tuning paradigm is based on the well-known delta rule but includes a modification to the learning rate parameter plus a correction term. It guarantees tracking as well as bounded NN weights in non-ideal situations, so that a persistency of excitation condition on the internal signals is not needed. Simulation results are presented in order to verify the theoretical conclusions

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994