Sliding mode control of nonlinear systems using Gaussian radial basis function neural networks

Author

Efe, M. Önder ; Kaynak, Okyay ; Yu, Xinghuo ; Wilamowski, Bogdan M.

Author_Institution

Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA

Volume

1

fYear

2001

fDate

2001

Firstpage

474

Abstract

A method for driving the dynamics of a nonlinear system to a sliding mode is discussed. The approach is based on a sliding mode control methodology, i.e., the system under control is driven towards a sliding mode by tuning the parameters of the controller. In this loop, the parameters of the controller are adjusted such that a zero learning error level is reached in one dimensional phase space defined on the output of the controller. A Gaussian radial basis function neural network is used as the controller

Keywords

dynamics; neurocontrollers; nonlinear control systems; radial basis function networks; tuning; variable structure systems; Gaussian radial basis function neural networks; dynamics; nonlinear systems; one dimensional phase space; sliding mode control; zero learning error level; Actuators; Computer networks; Control systems; Educational institutions; Informatics; Nonlinear dynamical systems; Nonlinear systems; Radial basis function networks; Sliding mode control; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.939066

Filename

939066