DocumentCode
2850731
Title
Vehicle lateral stability control based on single neuron network
Author
Zhang, Jinzhu ; Zhang, Hongtian
Author_Institution
Harbin Eng. Univ., Harbin, China
fYear
2010
fDate
26-28 May 2010
Firstpage
290
Lastpage
293
Abstract
According to the nonlinear and parameter time-varying characteristics of vehicle lateral stability control, a novel algorithm of vehicle lateral stability control based on single neuron network was proposed. Based on self-learning and adaptive ability of single neural network, the parameters of vehicle lateral stability controller were self-tuning on-line and the problem of large computation time brought by traditional PID control was avoided, in which the parameters of reference model of the controlled system must be identified with large calculation burden. The hardware in loop simulation platform is established based on the LabVIEW system, and the vehicle lateral stability control system is tested on the platform. The results of the simulation show this algorithm can effectively make vehicle keep and track the desired direction, and has good robustness and adaptability for vehicle lateral stability control system.
Keywords
adaptive control; learning systems; neurocontrollers; nonlinear control systems; stability; three-term control; time-varying systems; vehicles; LabVIEW system; PID control; adaptive ability; loop simulation platform; nonlinear time varying characteristics; parameter time varying characteristics; single neuron network; vehicle lateral stability control; Adaptive control; Computer networks; Control system synthesis; Hardware; Neural networks; Neurons; Programmable control; Robust stability; Three-term control; Vehicles; hardware in loop simulation; single neuron network; vehicle lateral stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location
Xuzhou
Print_ISBN
978-1-4244-5181-4
Electronic_ISBN
978-1-4244-5182-1
Type
conf
DOI
10.1109/CCDC.2010.5499067
Filename
5499067
Link To Document