• DocumentCode
    2917489
  • Title

    Research on Wheelbase Preview Control for Vehicle Semi-active Suspension Based on Neural Networks

  • Author

    Jun-ping, Feng ; Shao-yi, Bei ; Chuan-yi, Yuan ; Lan-chun, Zhang

  • Author_Institution
    Sch. of Mech. & Automobile Eng., Jiangsu Teachers Univ. of Technol., Changzhou, China
  • Volume
    3
  • fYear
    2009
  • fDate
    21-22 Nov. 2009
  • Firstpage
    290
  • Lastpage
    293
  • Abstract
    Based on the model of the 1/2 vehicle semi-active suspension with wheelbase preview, combined with neural networks algorithm and PID control theory, a neuron adaptive PID control algorithm was put forward, and designed the single neuron PID controller of semi-active suspension based on wheelbase preview. The simulation calculation was done at different speed, the simulation results showed that, compared with the passive system, the semi-active suspension wheelbase preview neurons PID controlled system can effectively reduce vehicle vibration, compared with the neuron adaptive PID controlled system, the rear vertical acceleration and pitch angular acceleration decreased significantly at the lower speed, improved the ride of the automobile.
  • Keywords
    adaptive control; control system synthesis; neural nets; suspensions (mechanical components); three-term control; vehicles; neural networks algorithm; neuron adaptive PID control algorithm; passive system; pitch angular acceleration; rear vertical acceleration; vehicle semi-active suspension; wheelbase preview control; Acceleration; Adaptive control; Algorithm design and analysis; Control system synthesis; Control theory; Neural networks; Neurons; Programmable control; Three-term control; Vehicles; Neural Networks; neuron; road surface roughness; suspension; wheelbase preview;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on
  • Conference_Location
    Nanchang
  • Print_ISBN
    978-0-7695-3859-4
  • Type

    conf

  • DOI
    10.1109/IITA.2009.351
  • Filename
    5369439