• DocumentCode
    2022347
  • Title

    High efficiency drives for synchronous reluctance motors using neural network

  • Author

    Senjyu, Tomonobu ; Shingaki, Takeshi ; Omoda, Akihiro ; Uezato, Katsumi

  • Author_Institution
    Ryukyus Univ., Okinawa, Japan
  • Volume
    2
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    777
  • Abstract
    A high efficiency drive technique for the synchronous reluctance motors (SRMs) using a neural network (NN) is presented in this paper. Since the NN can map the nonlinear relation, the high efficiency SRM drive does not require an accurate machine model. Moreover, the proposed method has robustness against machine parameter variations because the NN is learned on-line in this paper. The usefulness of the proposed method is verified by experiments
  • Keywords
    control system synthesis; learning (artificial intelligence); machine control; machine testing; machine theory; neurocontrollers; reluctance motor drives; machine parameter variations; neural network; nonlinear relation mapping; online learning; robustness; synchronous reluctance motor drives; AC motors; DC motors; Electronic mail; Equivalent circuits; Fuzzy control; Iron; Neural networks; Reluctance motors; Robustness; Velocity control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE
  • Conference_Location
    Nagoya
  • Print_ISBN
    0-7803-6456-2
  • Type

    conf

  • DOI
    10.1109/IECON.2000.972221
  • Filename
    972221