Adaptive backstepping control using neural networks

Author

Shuntian, Lou ; Xinhai, Chen ; Xianda, Zhang

Author_Institution

Key Lab. for Radar Signal Process., Xidian Univ., Xi´´an, China

Volume

2

fYear

2000

fDate

2000

Firstpage

1026

Abstract

This paper proposes an adaptive backstepping design method for a kind of affine nonlinear system with unknown nonlinearity and/or uncertainty. The neural network is used to approximate the unknown nonlinear function and/or uncertainty in the system. Then the neural adaptive backstepping controller without off-line training is obtained using conventional adaptive backstepping. According to the Lyapunov stability theory, the weight update law of neural network, the adaptive law of error bound, the stabilizing function and the control are obtained, thus the stability of closed-loop control system is ensured. The simulation result shows the effectiveness of adaptive backstepping control using neural networks

Keywords

Lyapunov methods; adaptive control; closed loop systems; function approximation; neurocontrollers; nonlinear systems; stability; uncertain systems; Lyapunov stability; adaptive control; affine nonlinear system; backstepping; closed-loop system; function approximation; neural network; neurocontrol; uncertain systems; weight update; Adaptive control; Adaptive systems; Backstepping; Control systems; Design methodology; Lyapunov method; Neural networks; Nonlinear systems; Programmable control; 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.2000.863391

Filename

863391