• DocumentCode
    2551608
  • Title

    Internal model control based on RBF neural network inverse system decoupling in a 3-DOf helicopter system

  • Author

    Dong, Xiucheng ; Zhao, Yunyuan ; Rui, Guangzheng

  • Author_Institution
    Provincial Key Lab. on Signal & Inf. Process., Xihua Univ., Chengdu, China
  • fYear
    2011
  • fDate
    21-25 June 2011
  • Firstpage
    570
  • Lastpage
    574
  • Abstract
    3-DOF helicopter is a typical multi-input multi-output (MIMO) system with high-order and strong channel coupling and nonlinearity. In this paper, an internal control strategy based on RBF neural network inverse system decoupling for helicopter process is proposed. First the mathematical model of helicopter system is obtained and the reversibility of system is testified, then the neural network inverse system of helicopter is established by neural network online identification, and the inverse model as controller model and helicopter in series, which forms a dynamic pseudo linear system. The MIMO helicopter system with strong coupling is converted into isolated dynamic decoupling pseudo linear system. Finally, a linear close-loop internal model controller is designed. The simulation shows that this strategy is very validity in tracking control of the 3-DOF helicopter system.
  • Keywords
    MIMO systems; closed loop systems; control system synthesis; helicopters; identification; mathematical analysis; neurocontrollers; radial basis function networks; vibrational modes; 3-DOf helicopter system; MIMO helicopter system; RBF neural network inverse system decoupling; helicopter process; high-order coupling; high-order nonlinearity; isolated dynamic decoupling pseudo linear system; linear close-loop internal model controller; mathematical model; multiinput multioutput system; neural network online identification; strong channel coupling; strong channel nonlinearity; Adaptation models; Artificial neural networks; Control systems; Couplings; Helicopters; Linear systems; Mathematical model; 3-DOF helicopter; Inverse system; RBF neural networks; internal model control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2011 9th World Congress on
  • Conference_Location
    Taipei
  • Print_ISBN
    978-1-61284-698-9
  • Type

    conf

  • DOI
    10.1109/WCICA.2011.5970577
  • Filename
    5970577