Adaptive Control of a Class of Nonaffine Systems

Author

Zibin, Xu ; Jianqing, Min

Author_Institution

Modern Educ. Technol. Center, Zhejiang Shuren Univ., Hangzhou

fYear

2008

fDate

21-22 Dec. 2008

Firstpage

62

Lastpage

65

Abstract

An adaptive backstepping neural controller design is presented for a class of nonaffine nonlinear system with mismatched uncertainties. By applying backstepping design strategy and online approaching uncertainties with fully tuned radial basis function (RBF) neural networks (NNs), the adaptive tuning rules are derived from the Lyapunov stability theory. A nonlinear tracking differentiator is introduced to deal with the problem of extremely expanded operation quantity of backstepping method. The developed control scheme guarantees that all the signals of the closed-loop system are uniform ultimate boundedness. Simulation results are provided to show the good tracking performance and effectiveness of the proposed approach.

Keywords

Lyapunov methods; adaptive control; closed loop systems; control system synthesis; neurocontrollers; nonlinear control systems; radial basis function networks; stability; time-varying systems; Lyapunov stability theory; adaptive backstepping neural controller design; adaptive tuning rules; closed-loop system; nonaffine nonlinear system; nonlinear tracking differentiator; radial basis function neural networks; Adaptive control; Adaptive systems; Backstepping; Control systems; Lyapunov method; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Uncertainty; adaptive control; backstepping; fully tuned RBF NNs; nonaffine systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008. IEEE International Symposium on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-3530-2

Electronic_ISBN

978-1-4244-3531-9

Type

conf

DOI

10.1109/KAMW.2008.4810425

Filename

4810425