Adaptive neuro sliding mode control of uncertain nonlinear system with dead zone input

Author

Xu, Zibin ; Ruan, Jian ; Min, Jianqin

Author_Institution

MOE Key Lab. of Mech. Manuf. & Autom., Zhejiang Univ. of Technol., Hangzhou

fYear

2008

fDate

25-27 June 2008

Firstpage

4723

Lastpage

4726

Abstract

Aiming at the uncertain nonlinear system with a dead zone input, a design method of adaptive neuro sliding mode control is presented to combine neural network theory with sliding mode control theory. RBF neural networks are used to realize modeling of nondeterministic system. Adaptive laws are derived based on Lyapunov stability theory which guarantees the stability of control system. Theoretical analysis and simulation results indicate that the control approach can be applied to the systems either with or without series nonlinearity and/or dead zone in the input.

Keywords

Lyapunov methods; adaptive control; control system synthesis; neurocontrollers; nonlinear control systems; radial basis function networks; uncertain systems; variable structure systems; Lyapunov stability theory; RBF neural networks; adaptive neuro sliding mode control; dead zone input; design method; neural network theory; theoretical analysis; uncertain nonlinear system; Adaptive control; Adaptive systems; Control systems; Design methodology; Lyapunov method; Neural networks; Nonlinear systems; Programmable control; Sliding mode control; Stability; adaptive neuro sliding mode control; dead zone; sliding mode control; uncertain nonlinear system;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4594533

Filename

4594533