Dual mode adaptive control with Gaussian networks

Author

Hsu, Liu ; Real, Jose A.

Author_Institution

Dept. of Electr. Eng., Univ. Fed. do Rio de Janeiro, Brazil

Volume

4

fYear

1997

fDate

10-12 Dec 1997

Firstpage

4032

Abstract

An adaptive control structure called dual mode adaptive control (DMAC) is proposed for a class of nonlinear systems. A Gaussian neural network is used to adaptively compensate the plant nonlinearity. The network learning strategy is based on a combination of parameter adaptation learning with variable structure control. The proposed controller is compared to a controller based on a convex combination of variable structure and parameter adaptive laws. As an application, we focus on the problem of nonlinearly parametrized systems

Keywords

adaptive control; control nonlinearities; feedforward neural nets; learning (artificial intelligence); neurocontrollers; nonlinear control systems; variable structure systems; Gaussian neural network; adaptive compensation; dual mode adaptive control; network learning strategy; nonlinear systems; nonlinearly parametrized systems; parameter adaptation learning; plant nonlinearity; variable structure control; Adaptive control; Adaptive systems; Control systems; Frequency; Integrated circuit modeling; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Real time systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on

Conference_Location

San Diego, CA

ISSN

0191-2216

Print_ISBN

0-7803-4187-2

Type

conf

DOI

10.1109/CDC.1997.652497

Filename

652497