Recurrent neural control for rollover prevention on heavy vehicles

Author

Sanchez, Edgar N. ; Ricalde, Luis J. ; Langari, Reza ; Shahmirzad, Danial

Author_Institution

CINVESTAV, Unidad Guadalajara, Jalisco, Mexico

Volume

3

fYear

2004

fDate

25-29 July 2004

Firstpage

1841

Abstract

An active control system is developed to prevent rollover in heavy vehicles. A high order recurrent neural network is used to model the unknown tractor semitrailer system; a learning law is obtained using the Lyapunov methodology. Then a control law, which stabilizes the reference tracking error dynamics, is developed using control Lyapunov functions. The control scheme is applied to the speed and speed-yaw rate trajectory tracking in a tractor-semitrailer during a cornering situation.

Keywords

Lyapunov methods; agricultural machinery; neurocontrollers; position control; recurrent neural nets; road vehicles; velocity control; active control system; control Lyapunov function; heavy vehicles; high order recurrent neural network; learning law; recurrent neural control; rollover prevention; speed control; speed-yaw rate trajectory tracking; tracking error dynamics; tractor semitrailer system; Adaptive control; Control systems; Nonlinear control systems; Optimal control; Recurrent neural networks; Sliding mode control; Trajectory; Uncertainty; Vehicle dynamics; Velocity control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1380889

Filename

1380889