A new method of training direct neuro-controllers

Author

Bahrami, Mohammad

Author_Institution

Sch. of Electr. Eng., New South Wales Univ., Kensington, NSW, Australia

Volume

4

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

2655

Abstract

A new method of training neuro-controllers for nonlinear plants is proposed. Using this controller does not require identification of the plant or its inverse model. Direct inverse controllers discussed in the literature require the Jacobian of the plant or the sign of the Jacobian which may not be available for an unknown plant. The authors approximate the output of the plant with the output of its reference model in a model reference model in an adaptive control scheme and use the Jacobian of the reference model instead of the plant. Simulation results show a satisfactory performance

Keywords

learning (artificial intelligence); model reference adaptive control systems; neurocontrollers; nonlinear control systems; Jacobian; adaptive control; direct neuro-controllers; model reference model; nonlinear plants; training; Adaptive control; Artificial neural networks; Australia; Backpropagation; Delay effects; Error correction; Jacobian matrices; Kernel; Multilayer perceptrons; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374641

Filename

374641