Title :
Research on Suspension System Based on Genetic Algorithm and Neural Network Control
Author :
Tang, Chuan Yin ; Zhao, Guang Yao ; Li, Hua ; Zhou, Shu Wen
Author_Institution :
Sch. of Mech. Eng. & Autom., North Eastern Univ., Shenyang, China
Abstract :
An active suspension system for vehicles using the genetic algorithms and neural network controls strategy is presented. A half car four degree of freedom suspension vibration model is described. Compared with the conventional passive suspension system, the analysis is done to the system control performance. The analysis of the system response is obtained through the change of the neural network training coefficients, genetic algorithms input functions and the change of velocity. The simulation results indicate that the vehicle vibration can be reduced and the ride comfort is improved by the proposed suspension systems.
Keywords :
automobiles; genetic algorithms; learning (artificial intelligence); neurocontrollers; suspensions (mechanical components); vibration control; active suspension system; automobile; genetic algorithm; neural network control; neural network training coefficient; ride comfort; vehicle vibration model; Automatic control; Automation; Control systems; Damping; Genetic algorithms; Mechanical engineering; Neural networks; Road vehicles; Shock absorbers; Tires; active suspension; genetic algorithm; neural networks; simulation;
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Conference_Location :
Changsha, Hunan
Print_ISBN :
978-0-7695-3804-4
DOI :
10.1109/ICICTA.2009.120