• DocumentCode
    504764
  • Title

    Adaptive friction observer and sliding mode controller development with RFNN for nonlinear friction compensation

  • Author

    Han, Seong Ik ; Cho, Young Su ; Jin, Seong Min ; Lee, Chang Don ; Yang, Soon Yong

  • Author_Institution
    Dept. of Electr. Autom., Suncheon First Coll., Cheonnam, South Korea
  • fYear
    2009
  • fDate
    18-21 Aug. 2009
  • Firstpage
    4971
  • Lastpage
    4976
  • Abstract
    In this paper, the adaptive friction compensation schemes are developed to provide much enhanced position tracking performance against nonlinear dynamic friction. The adaptive friction parameter observer possessing a simple structure and to be easy to implementation into controller is first studied to estimate the friction parameters. The process of the uncertainty approximation using the RFNN technique is considered to enhance the positioning performance. Suppressing additional unknown friction uncertainty by the RFNN, the favorable position tracking result can be achieved via some simulation and experiment to the rotary servo mechanical system.
  • Keywords
    adaptive control; compensation; friction; fuzzy neural nets; machine control; neurocontrollers; nonlinear control systems; observers; recurrent neural nets; servomechanisms; uncertain systems; variable structure systems; adaptive friction compensation; adaptive friction parameter observer; friction parameter estimation; friction uncertainty; nonlinear friction compensation; position tracking; recurrent fuzzy neural network; rotary servo mechanical system; sliding mode controller; uncertainty approximation; Adaptive control; Control systems; Friction; Fuzzy control; Fuzzy neural networks; Hysteresis; Neural networks; Programmable control; Servomechanisms; Sliding mode control; Adaptive friction observer; LuGre friction model; Recurrent fuzzy neural networks; Sliding mode control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    ICCAS-SICE, 2009
  • Conference_Location
    Fukuoka
  • Print_ISBN
    978-4-907764-34-0
  • Electronic_ISBN
    978-4-907764-33-3
  • Type

    conf

  • Filename
    5334646