Sliding Mode Neural Network Control for Nonlinear Systems

Author

Gang, Chen

Author_Institution

Coll. of Autom., Chongqing Univ.

fYear

2006

fDate

25-28 June 2006

Firstpage

2476

Lastpage

2480

Abstract

An adaptive sliding mode neural network (NN) control scheme is proposed for a class of nonlinear systems with mismatched uncertainties. By applying the smooth projection algorithm and the integral-type Lyapunov function, the parameter drift and controller singularity problems are avoided perfectly. It is proved that convergence of tracking error and boundedness of all the signals in the closed-loop system can be guaranteed with the proposed controller. Simulation results demonstrate the effectiveness of the presented control strategy

Keywords

Lyapunov methods; adaptive control; closed loop systems; neurocontrollers; nonlinear control systems; uncertain systems; variable structure systems; adaptive sliding mode neural network control; closed-loop system; controller singularity problems; integral-type Lyapunov function; mismatched uncertainties; nonlinear system; parameter drift; smooth projection algorithm; Adaptive control; Adaptive systems; Control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Projection algorithms; Sliding mode control; Uncertainty; Neural network; Nonlinear systems; Sliding mode control;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation, Proceedings of the 2006 IEEE International Conference on

Conference_Location

Luoyang, Henan

Print_ISBN

1-4244-0465-7

Electronic_ISBN

1-4244-0466-5

Type

conf

DOI

10.1109/ICMA.2006.257740

Filename

4026489