• DocumentCode
    1910324
  • Title

    Identification and control of induction machines using artificial neural networks

  • Author

    Wishart, Michael T. ; Harley, Ronald G.

  • Author_Institution
    Dept. of Electr. Eng., Natal Univ., Durban, South Africa
  • fYear
    1993
  • fDate
    2-8 Oct 1993
  • Firstpage
    703
  • Abstract
    The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme
  • Keywords
    adaptive control; electric current control; identification; induction motors; machine control; nonlinear control systems; velocity control; adaptive control; artificial neural networks; current controller; identification; induction motor control; nonlinear controller; rotor speed control; standard vector control scheme; stator currents control; Africa; Artificial neural networks; Brain modeling; Control system synthesis; Control systems; Electromagnetic modeling; Induction machines; Machine vector control; Nonlinear dynamical systems; Stators;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industry Applications Society Annual Meeting, 1993., Conference Record of the 1993 IEEE
  • Conference_Location
    Toronto, Ont.
  • Print_ISBN
    0-7803-1462-X
  • Type

    conf

  • DOI
    10.1109/IAS.1993.298875
  • Filename
    298875