• DocumentCode
    3477503
  • Title

    Genetic algorithms-based fuzzy neural network sliding mode control for brushless doubly fed machine

  • Author

    Shao, Zongkai

  • Author_Institution
    Sch. of Hydropower & Inf. Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
  • Volume
    3
  • fYear
    2010
  • fDate
    12-13 June 2010
  • Firstpage
    467
  • Lastpage
    475
  • Abstract
    In this paper, because a genetic algorithms-based fuzzy neural network control is incorporated into the sliding mode control (SMC) to adaptively regulate the adaptive law of SMC, a genetic algorithm fuzzy neural network sliding mode controller (GAFNSMC) for brushless doubly fed machine (BDFM) adjustable speed system is presented. The proposed controller for BDFM eliminates the average chattering encountered by most SMC schemes, and employs the robustness and excellent static and dynamic performances of SMC. Simulation results show that the proposed control strategy is of the feasibility, correctness and effectiveness.
  • Keywords
    AC motors; brushless machines; fuzzy neural nets; genetic algorithms; machine control; neurocontrollers; variable structure systems; velocity control; adjustable speed system; brushless doubly fed machine; fuzzy neural network; genetic algorithms; sliding mode control; Niobium; Robustness; brushless doubly fed machines (BDFM); dynamic model; fuzzy neural network control; genetic algorithms; sliding mode control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-6944-4
  • Type

    conf

  • DOI
    10.1109/CCTAE.2010.5544317
  • Filename
    5544317