Feedback-error-learning control with considering smoothness of unknown nonlinearities

Author

Kuroe, Yasuaki ; Inayoshi, Hidehisa ; Mori, Takehiro

Author_Institution

Dept. of Electron. & Inf. Sci., Kyoto Inst. of Technol., Japan

Volume

4

fYear

1997

fDate

9-12 Jun 1997

Firstpage

2402

Abstract

Learning control of nonlinear systems by using neural networks has been widely studied. Among them the feedback-error-learning control proposed by Kawato et al. (1987), has been recognized to be an excellent learning method because of the fact that this method makes it possible to realize inverse models of unknown nonlinear controlled objects on neural networks. Since forward or inverse models of controlled objects, in general, are expressed by nonlinear smooth functions, taking account of the smoothness of forward or inverse models in the learning control would improve its performance considerably. In this paper the feedback error-learning control is extended so as to being able to treat the smoothness of unknown nonlinearity of controlled objects. The proposed method makes it possible to realize inverse models more accurately and to attain more precise control

Keywords

control nonlinearities; feedback; learning (artificial intelligence); learning systems; neurocontrollers; nonlinear control systems; nonlinear differential equations; nonlinear dynamical systems; uncertain systems; feedback-error-learning control; forward models; inverse models; nonlinear smooth functions; smoothness; unknown nonlinear controlled objects; unknown nonlinearities; Control nonlinearities; Control systems; Education; Electronic mail; Information science; Inverse problems; Learning systems; Linear feedback control systems; Neural networks; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.614445

Filename

614445