Adaptive Neuro Sliding Mode Control of Nonlinear System

Author

Xu Zi-bin ; Min Jian-qing ; Ruan Jian

Author_Institution

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

Volume

1

fYear

2008

fDate

20-22 Oct. 2008

Firstpage

284

Lastpage

288

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 and nonlinear 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; RBF neural network; adaptive law; adaptive neuro sliding mode control; control design; modeling; nondeterministic system; nonlinear system; uncertain 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; uncertain nonlinear system;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference on

Conference_Location

Hunan

Print_ISBN

978-0-7695-3357-5

Type

conf

DOI

10.1109/ICICTA.2008.195

Filename

4659490