• DocumentCode
    3391612
  • Title

    Hybrid LQG-neural controller for inverted pendulum system

  • Author

    Sazonov, E.S. ; Klinkhachorn, P. ; Klein, R.L.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Clarkson Univ., Potsdam, NY, USA
  • fYear
    2003
  • fDate
    16-18 March 2003
  • Firstpage
    206
  • Lastpage
    210
  • Abstract
    The paper presents a hybrid system controller, incorporating a neural and an LQG controller. The neural controller has been optimized by genetic algorithms directly on the inverted pendulum system. The failure-free optimization process stipulated a relatively small region of the asymptotic stability of the neural controller, which is concentrated around the regulation point. The presented hybrid controller combines benefits of a genetically optimized neural controller and an LQG controller in a single system controller. High quality of the regulation process is achieved through utilization of the neural controller, while stability of the system during transient processes and a wide range of operation are assured through application of the LQG controller. The hybrid controller has been validated by applying it to a simulation model of an inherently unstable system - the inverted pendulum.
  • Keywords
    asymptotic stability; genetic algorithms; linear quadratic Gaussian control; motion control; multilayer perceptrons; neurocontrollers; nonlinear control systems; pendulums; position control; failure-free optimization process; genetic algorithms; hybrid LQG-neural controller; inherently unstable system; inverted pendulum system; regulation process; stability; transient processes; Control system synthesis; Control systems; Equations; Genetic algorithms; Neural networks; Numerical models; Optimization methods; Sliding mode control; Stability; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Theory, 2003. Proceedings of the 35th Southeastern Symposium on
  • ISSN
    0094-2898
  • Print_ISBN
    0-7803-7697-8
  • Type

    conf

  • DOI
    10.1109/SSST.2003.1194559
  • Filename
    1194559