• 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