• DocumentCode
    406780
  • Title

    On-line instant torque estimation of switched reluctance motor using adaptive B-spline neural network

  • Author

    Lin, Zhengyu ; Reay, Donald S. ; Williams, Barry W. ; He, Xiangning

  • Author_Institution
    Dept. of Electr., Electron. & Comput. Eng., Heriot-Watt Univ., Edinburgh, UK
  • Volume
    2
  • fYear
    2003
  • fDate
    2-6 Nov. 2003
  • Firstpage
    1033
  • Abstract
    A novel on-line instant torque estimation scheme for a switched reluctance motor (SRM) is presented. In the proposed method, an adaptive B-spline neural network is used to learn the non-linear flux-linkage and torque characteristics of a SRM. Due to the local nature of its generalisation properties, the training of the B-spline neural network is accomplished on-line and in real-time, and the system does not require a priori knowledge of the machine´s electromagnetic characteristics. The potential of the method is demonstrated successfully in simulation and experimentally using a 300 W 12/8 3-phase SRM.
  • Keywords
    estimation theory; learning (artificial intelligence); magnetic flux; neural nets; power engineering computing; reluctance motors; splines (mathematics); torque; 300 W; SRM; adaptive B-spline neural network; electromagnetic characteristics; nonlinear flux-linkage; online instant torque estimation; switched reluctance motor; torque characteristics; Adaptive systems; Computer networks; Couplings; Neural networks; Phase estimation; Physics computing; Reluctance machines; Reluctance motors; Spline; Torque measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics Society, 2003. IECON '03. The 29th Annual Conference of the IEEE
  • Print_ISBN
    0-7803-7906-3
  • Type

    conf

  • DOI
    10.1109/IECON.2003.1280187
  • Filename
    1280187