Vehicle stability sliding mode control based on RBF neural network

Author

Jinzhu, Zhang ; Hongtian, Zhang

Author_Institution

Power & Energy Coll., Harbin Univ. of Eng., Harbin, China

Volume

4

fYear

2010

fDate

27-29 March 2010

Firstpage

243

Lastpage

246

Abstract

According to the nonlinear and parameter time-varying characteristics of vehicle stability control, a sliding control algorithm is proposed based on radial base function (RBF) neural network. The algorithm not only can reduce the chattering caused by the conventional sliding mode, but also improve the robust of the adaptive neural network control. The simulation results show the algorithm ensures that the car could run at the direction desired by the drivers.

Keywords

adaptive control; neurocontrollers; nonlinear control systems; radial basis function networks; stability; time-varying systems; variable structure systems; vehicles; RBF neural network; adaptive neural network control; chattering reduction; nonlinear time-varying characteristics; parameter time-varying characteristics; radial base function neural network; vehicle stability sliding mode control; Automatic control; Automotive engineering; Educational institutions; Frequency; Neural networks; Power engineering and energy; Robust control; Sliding mode control; Stability; Vehicle driving; neural network; nonlinearity; radial base function; vehicle stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Control (ICACC), 2010 2nd International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-5845-5

Type

conf

DOI

10.1109/ICACC.2010.5486963

Filename

5486963