• DocumentCode
    2440624
  • Title

    Impedance Control of Exoskeleton Suit Based on Adaptive RBF Neural Network

  • Author

    Yang, Zhiyong ; Zhu, Yuguang ; Yang, Xiuxia ; Zhang, Yuanshan

  • Author_Institution
    Dept. of Strategy Missle Eng., Naval Aeronaut. & Astronaut. Univ., Yantai, China
  • Volume
    1
  • fYear
    2009
  • fDate
    26-27 Aug. 2009
  • Firstpage
    182
  • Lastpage
    187
  • Abstract
    Exoskeleton suit is a typical human-machine system. Control the exoskeleton suit to track the pilot´s moving trajectory as well as to minimize the human-machine interaction force. The suit will help decrease the pilot´s power consumption and assist the pilot to carry heavy load. Impedance control was introduced to the control of exoskeleton suit. As the control laws that based on the dynamic model without model uncertainty compensation will increase the human-machine force, a RBF neural network with adaptive learning algorithm was used to compensate the model uncertainty. The stability analysis of the control law was given and the simulation results show the feasibility and validity of the proposed control law.
  • Keywords
    human-robot interaction; neurocontrollers; robot dynamics; stability; adaptive RBF neural network; adaptive learning algorithm; exoskeleton robot; exoskeleton suit; human-machine interaction force; human-machine system; impedance control; stability analysis; Adaptive control; Adaptive systems; Exoskeletons; Force control; Impedance; Man machine systems; Neural networks; Programmable control; Trajectory; Uncertainty; RBF neural network; exoskeleton suit; human-machine system; impedance control; uncertainties;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Human-Machine Systems and Cybernetics, 2009. IHMSC '09. International Conference on
  • Conference_Location
    Hangzhou, Zhejiang
  • Print_ISBN
    978-0-7695-3752-8
  • Type

    conf

  • DOI
    10.1109/IHMSC.2009.54
  • Filename
    5336076