• DocumentCode
    1371040
  • Title

    Implementation of a neural network to adaptively identify and control VSI-Fed induction motor stator currents

  • Author

    Burton, Bruce ; Harley, Ronald G. ; Diana, Gregory ; Rodgerson, James L.

  • Author_Institution
    Dept. of Electr. Eng., Natal Univ., Durban, South Africa
  • Volume
    34
  • Issue
    3
  • fYear
    1998
  • Firstpage
    580
  • Lastpage
    588
  • Abstract
    This paper presents a prototype hardware implementation of a continually online trained artificial neural network (ANN) to adaptively identify the electrical dynamics of an induction machine and control its stator currents from a pulsewidth modulated voltage-source inverter. A single-transputer-based hardware platform is described, and the effects of computational speed limitations on the controller bandwidth are discussed. Captured results are compared with simulation results to practically verify the success of the adaptive neural network identification and control scheme
  • Keywords
    PWM invertors; adaptive control; identification; induction motors; learning (artificial intelligence); machine control; neurocontrollers; power engineering computing; VSI-fed induction motor; adaptive identification; adaptive neural network; controller bandwidth; electrical dynamics; neural network; online trained artificial neural network; pulsewidth modulated voltage-source inverter; single-transputer-based hardware platform; stator currents; Artificial neural networks; Induction machines; Neural network hardware; Neural networks; Prototypes; Pulse inverters; Pulse modulation; Pulse width modulation inverters; Stators; Voltage control;
  • fLanguage
    English
  • Journal_Title
    Industry Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0093-9994
  • Type

    jour

  • DOI
    10.1109/28.673729
  • Filename
    673729