Direct adaptive control for a class of nonlinear systems using multilayer neural networks

Author

Zhang, Tianping ; Shen, Qikuen ; Mei, Jiandong ; Yi, Yang

Author_Institution

Dept. of Comput., Yangzhou Univ., China

Volume

1

fYear

2004

fDate

6-9 Dec. 2004

Firstpage

7

Abstract

A new design scheme of direct adaptive neural network controller for a class of nonlinear systems with unknown function control gain is proposed in this paper. The design is based on the principle of sliding mode control and the approximation capability of multilayer neural networks (MNNs). By adopting the adaptive compensation term of the upper bound function of the sum of residual and approximation error, the closed-loop control system is shown to be globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.

Keywords

adaptive control; closed loop systems; compensation; control system synthesis; neurocontrollers; nonlinear control systems; stability; variable structure systems; adaptive compensation; approximation error; closed-loop control system; direct adaptive neural network controller; function control gain; global stability; multilayer neural networks; nonlinear systems; residual error; sliding mode control; Adaptive control; Adaptive systems; Control systems; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Sliding mode control; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th

Print_ISBN

0-7803-8653-1

Type

conf

DOI

10.1109/ICARCV.2004.1468789

Filename

1468789