Neural networks direct adaptive control for a class of MIMO uncertain nonlinear systems

Author

Hu, Tingliang ; Zhu, Jihong ; Hu, Chunhua ; Sun, Zengqi

Author_Institution

Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China

fYear

2005

fDate

28-30 June 2005

Firstpage

148

Lastpage

153

Abstract

This paper presents a direct adaptive control scheme based on multi-layer neural networks for a class of multi-input multi-output (MIMO) nonlinear systems with unknown nonlinearity. The on-line updating rules of the neural networks parameters are obtained by Lyapunov stability theory. All signals in the closed-loop system are bounded and the output tracking error converges to a small neighborhood of zero. In this sense the closed-loop system is stable. The effectiveness of the control scheme is verified by a simulation of two link manipulator.

Keywords

Lyapunov methods; MIMO systems; adaptive control; closed loop systems; manipulators; neural nets; nonlinear systems; stability; uncertain systems; Lyapunov stability theory; MIMO uncertain nonlinear system; closed-loop system; direct adaptive control scheme; multiinput multioutput nonlinear system; multilayer neural network; neural network; two link manipulator simulation; Adaptive control; Control systems; Lyapunov method; MIMO; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear systems; Stability; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing in Industrial Applications, 2005. SMCia/05. Proceedings of the 2005 IEEE Mid-Summer Workshop on

Print_ISBN

0-7803-8942-5

Type

conf

DOI

10.1109/SMCIA.2005.1466964

Filename

1466964