Stable adaptive control for nonlinear systems using neural networks

Author

Yang, Shi ; Chundi, Mu ; Weisheng, Yan ; Jun, Li ; Demin, Xu

Author_Institution

Dept. of Autom., Tsinghua Univ., Beijing, China

Volume

2

fYear

2000

fDate

2000

Firstpage

979

Abstract

Stability analysis of neural-network-based nonlinear control has presented great difficulties. For a class of affine nonlinear systems with uncertainties, we employed nonlinear-parameter-neural-networks (NPNN) to approximate online the unknown nonlinearities, estimate online the NPNN approximation error´s bound, and then succeeded in designing the control law and the adaptive laws of NPNN´s weights and the NPNN approximation error´s bound. The stability of the closed-loop is proved by using Lyapunov theory. Simulation results show that the controller we proposed exhibits excellent tracking performance

Keywords

Lyapunov methods; adaptive control; closed loop systems; control system analysis; neurocontrollers; nonlinear control systems; stability; uncertain systems; Lyapunov theory; NPNN; affine nonlinear systems; closed-loop stability; neural networks; nonlinear-parameter-neural-networks; online approximation; online estimation; stable adaptive control; uncertainties; unknown nonlinearities; Adaptive control; Approximation error; Control nonlinearities; Control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Stability analysis; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on

Conference_Location

Hefei

Print_ISBN

0-7803-5995-X

Type

conf

DOI

10.1109/WCICA.2000.863380

Filename

863380