• DocumentCode
    3506623
  • Title

    Identification of induction motor speed using neural networks

  • Author

    Ben-Brahim, Lazhar ; Kurosawa, Ryoichi

  • Author_Institution
    Heavy Apapratus Eng. Lab., Toshiba Corp., Tokyo, Japan
  • fYear
    1993
  • fDate
    19-21 April 1993
  • Firstpage
    689
  • Lastpage
    694
  • Abstract
    A newly developed approach to identify the mechanical speed of an induction motor based on the neural networks technique is described. The backpropagation neural network technique is used to provide a real-time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is backpropagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The backpropagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. A theoretical analysis as well as simulation results to verify the effectiveness of the new method are described in this paper.<>
  • Keywords
    backpropagation; control system analysis; digital control; induction motors; machine control; machine theory; neural nets; parameter estimation; real-time systems; rotors; velocity control; backpropagation; control system analysis; digital control; induction motor; machine control; machine theory; mechanical speed; neural networks; parameter estimation; real-time; rotor; simulation; velocity control; Adaptive estimation; Analytical models; Feedforward neural networks; Induction motors; Multi-layer neural network; Neural networks; Process control; Rotors; State estimation; Stators;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Conversion Conference, 1993. Yokohama 1993., Conference Record of the
  • Conference_Location
    Yokohama, Japan
  • Print_ISBN
    0-7803-0471-3
  • Type

    conf

  • DOI
    10.1109/PCCON.1993.264173
  • Filename
    264173