• DocumentCode
    182798
  • Title

    Adaptive fuzzy iterative learning controller for X-Y table position control

  • Author

    Elshazly, Osama ; El-bardini, Mohammad ; El-Rabaie, Nabila M.

  • Author_Institution
    Ind. Electron. & Control Eng. Dept., Menoufiya Univ., Menoufiya, Egypt
  • fYear
    2014
  • fDate
    22-24 May 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    In this paper, an adaptive fuzzy iterative learning control algorithm is proposed for controlling one of the Mecha-tronics systems. The proposed control scheme is based upon a proportional-derivative-integral (PID) iterative learning control (ILC), for which a fuzzy control is added to tune the parameters of the PID-type ILC. Moreover, an adaptation law is added to the fuzzy control in order to automatically select the proper fuzzy membership functions. The performance of proposed algorithm was assessed in computer numerical controlled (CNC) machine X-Y table to illustrate the validation and the effectiveness of the proposed procedure. The simulation results show that the proposed algorithm can reduce the trajectory error in a far less number of iterations.
  • Keywords
    adaptive control; computerised numerical control; fuzzy control; iterative methods; learning systems; mechatronics; three-term control; trajectory control; CNC machine X-Y table; PID iterative learning control; X-Y table position control; adaptation law; adaptive fuzzy iterative learning control algorithm; adaptive fuzzy iterative learning controller; computer numerical controlled machine X-Y table; fuzzy membership function; mechatronics system; proportional-derivative-integral iterative learning control; trajectory error; Actuators; DC motors; Friction; Fuzzy control; Heuristic algorithms; Process control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on
  • Conference_Location
    Cluj-Napoca
  • Print_ISBN
    978-1-4799-3731-8
  • Type

    conf

  • DOI
    10.1109/AQTR.2014.6857832
  • Filename
    6857832