• DocumentCode
    2378732
  • Title

    Position sensorless control of a SRM drive using ANN-techniques

  • Author

    Bellini, A. ; Filippetti, E. ; Franceschini, G. ; Tassoni, C. ; Vas, P.

  • Author_Institution
    Dept. of Eng. & Inf., Parma Univ., Italy
  • Volume
    1
  • fYear
    1998
  • fDate
    12-15 Oct. 1998
  • Firstpage
    709
  • Abstract
    Nowadays speed-sensorless electromechanical drives are feasible for many applications. The elimination of the mechanical transducer increases reliability, reducing costs. As far as synchronous machine drives are concerned, this issue is critical since the rotor position is fundamental to derive the proper switching sequence. If an SRM is used as an actuator different sensorless techniques have been proposed, essentially based on the rotor position detection by reluctance variation. Since the SRM is a highly nonlinear machine, it is an ideal candidate for the application of artificial neural networks (ANNs). In this paper, the results of a mixed closed loop scheme for the sensorless identification of an SRM drive are presented. A radial basis function ANN has been used, since it is well suited to simulate a nonlinear system over a wide operating area. Then the proposed ANN is applied within a standard control scheme as a position sensor. Simulation results show the effectiveness of the proposed method, since good agreement is reached with experimental results obtained from an SRM drive with a standard position sensor.
  • Keywords
    control system analysis; control system synthesis; machine theory; machine vector control; neurocontrollers; parameter estimation; position control; radial basis function networks; reluctance motor drives; rotors; velocity control; SRM drive; artificial neural networks; control design; control simulation; mixed closed loop scheme; nonlinear system simulation; position sensorless control; radial basis function ANN; reluctance variation; rotor position detection; sensorless parameter identification; switched reluctance motors; Artificial neural networks; Neural networks; Reluctance machines; Reluctance motors; Rotors; Sensorless control; Stators; Strontium; Torque; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industry Applications Conference, 1998. Thirty-Third IAS Annual Meeting. The 1998 IEEE
  • Conference_Location
    St. Louis, MO, USA
  • ISSN
    0197-2618
  • Print_ISBN
    0-7803-4943-1
  • Type

    conf

  • DOI
    10.1109/IAS.1998.732405
  • Filename
    732405