Application of neural networks to nonlinear adaptive control systems

Author

Lu, Jianming ; Yahag, Takashi

Author_Institution

Graduate Sch. of Sci. & Technol., Chiba Univ., Japan

Volume

1

fYear

2000

fDate

2000

Firstpage

252

Abstract

This paper presents a method of model reference adaptive control for nonlinear systems using neural networks. The control input is given by the sum of the output of a model reference adaptive controller and the output of the neural network. The neural network is used to compensate the nonlinearity of the plant dynamics that is not taken into consideration in the usual model reference adaptive control. The role of the neural network is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems. New parallel neural networks are proposed

Keywords

learning (artificial intelligence); model reference adaptive control systems; neural net architecture; nonlinear control systems; parallel architectures; control input; learning; linearized model; model reference adaptive control; nonlinear adaptive control systems; nonlinear systems; nonlinearity compensation; output error minimisation; parallel neural networks; plant dynamics; Adaptive control; Control nonlinearities; Control system synthesis; Error correction; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Programmable control; Regulators;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-5747-7

Type

conf

DOI

10.1109/ICOSP.2000.894486

Filename

894486