Robust model reference adaptive control of robots based on neural network parametrization

Author

Ge, S.S. ; Lee, T.H.

Author_Institution

Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore

Volume

3

fYear

1997

fDate

4-6 Jun 1997

Firstpage

2006

Abstract

In this paper, a robust model reference adaptive controller is presented for robots based on neural network parametrization. The controller is based on applying direct adaptive techniques to a basic fixed controller for better control performance, while a sliding mode control is introduced to guarantee robust closed-loop stability. It is shown that if bounded basis function networks are used for the parallel NN, uniformly stable adaptation is assured and asymptotic tracking of the reference signal is achieved

Keywords

adaptive control; closed loop systems; feedforward neural nets; model reference adaptive control systems; neurocontrollers; nonlinear systems; robot dynamics; robust control; tracking; variable structure systems; asymptotic tracking; closed-loop systems; dynamics; model reference adaptive control; multiple axis joint robots; neural network; nonlinear systems; parametrization; robust control; sliding mode control; stability; Adaptive control; Electronic mail; Multi-layer neural network; Neural networks; Programmable control; Robot control; Robust control; Robust stability; Sliding mode control; Sparse matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.611040

Filename

611040