• DocumentCode
    2012151
  • Title

    Internal Model Control of PM Synchronous Motor Based on RBF Network optimized by Genetic Algorithm

  • Author

    Yu-Zhou, Li ; Yu-Tao, Luo ; Ke-Gan, Zhao ; Li Jim

  • Author_Institution
    South China Univ. of Technol., Guangzhou
  • fYear
    2007
  • fDate
    May 30 2007-June 1 2007
  • Firstpage
    3051
  • Lastpage
    3054
  • Abstract
    An internal model control method which is based on RBF network optimized by genetic algorithm is proposed to control the speed of the permanent magnet synchronous motor in this paper. As genetic algorithm is a global search and optimization algorithm which simulates the genetic and long-term evolvement process of biology. By the optimization of genetic algorithm, the optimal structure and parameters of the RBF network are achieved and the optimized RBF network is applied into the speed loop internal model control of the permanent magnet synchronous motor. Simulation results show that the proposed internal model controller can overcome the influence caused by nonlinear factor and time varying parameters, and provides the high-performance dynamic characteristics.
  • Keywords
    genetic algorithms; machine control; neurocontrollers; permanent magnet motors; radial basis function networks; synchronous motors; velocity control; RBF network; genetic algorithm; internal model control; permanent magnet synchronous motor; speed control; AC motors; Artificial neural networks; Biological system modeling; Control systems; Equations; Genetic algorithms; Permanent magnet motors; Radial basis function networks; Sliding mode control; Synchronous motors; PMSM; RBF network; genetic algorithm; internal model control; speed control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Automation, 2007. ICCA 2007. IEEE International Conference on
  • Conference_Location
    Guangzhou
  • Print_ISBN
    978-1-4244-0817-7
  • Electronic_ISBN
    978-1-4244-0818-4
  • Type

    conf

  • DOI
    10.1109/ICCA.2007.4376921
  • Filename
    4376921