A model-based neurocontrol approach for car-following collision prevention

Author

Kumarawadu, Sisil ; Lee, Tsu-Tian

Author_Institution

Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan

Volume

1

fYear

2004

fDate

21-23 March 2004

Firstpage

152

Abstract

This paper presents a model-based neurocontrol approach for car-following collision prevention systems for Intelligent Vehicle Highway Systems (IVHSs). The controller is synthesized using resolved-acceleration-like control popular in robotics, and an online adaptive neural module. A nominal mass of the vehicle is used as the only dynamic model information in the control. Neural module is designed to adaptively compensate for dynamic model discrepancies, and coupling effects due to lateral and yaw motions. Several simulation test results in the face of different driving conditions are presented to validate the controller.

Keywords

acceleration control; automated highways; automobiles; collision avoidance; neurocontrollers; vehicle dynamics; Intelligent Vehicle Highway Systems; acceleration control; car following control; collision prevention systems; controller; coupling effects; dynamic model discrepancy; dynamic model information; lateral motions; model based neurocontrol; online adaptive neural module; robotics; yaw motions; Adaptive control; Control system synthesis; Intelligent vehicles; Programmable control; Road accidents; Road transportation; Robot control; Testing; Vehicle dynamics; Weight control;

fLanguage

English

Publisher

ieee

Conference_Titel

Networking, Sensing and Control, 2004 IEEE International Conference on

ISSN

1810-7869

Print_ISBN

0-7803-8193-9

Type

conf

DOI

10.1109/ICNSC.2004.1297425

Filename

1297425