• DocumentCode
    2498401
  • Title

    DRNN network DTC in electromagnetic continuously variable transmission

  • Author

    Fu, Xingfeng ; Luo, Yutao ; Zhou, Sijia ; Zhang, Yinxian

  • Author_Institution
    Dept. of Automobile Eng., South China Univ. of Technol., Guangzhou
  • fYear
    2008
  • fDate
    25-27 June 2008
  • Firstpage
    7831
  • Lastpage
    7834
  • Abstract
    In order to reduce the serious fluctuation of torque, fluxes and stator current in electromagnetic continuously variable transmission motor direct torque control, an amendatory direct torque control method based on the diagonal recurrent neural network technology was presented in this paper. This method can reduce the torque and flux ripple in static run and enhance the performance of low speed. The simulation experimental results indicate that this method may be feasible alternative and high robust capabilities.
  • Keywords
    machine control; recurrent neural nets; torque control; DRNN network DTC; diagonal recurrent neural network technology; electromagnetic continuously variable transmission; flux ripple; motor direct torque control; Converters; Engines; Equations; Mechanical power transmission; Reluctance motors; Rotors; Shafts; Stator windings; Torque control; Vehicles; Direct Torque Control; Motor Timing System; Robust Characteristic;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4244-2113-8
  • Electronic_ISBN
    978-1-4244-2114-5
  • Type

    conf

  • DOI
    10.1109/WCICA.2008.4594151
  • Filename
    4594151