• DocumentCode
    1663654
  • Title

    RLS-ESN based PID control for rehabilitation robotic arms driven by PM-TS actuators

  • Author

    Wu, Jun ; Huang, Jian ; Wang, Yongji ; Xing, Kexin

  • Author_Institution
    Dept. of Control Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China
  • fYear
    2010
  • Firstpage
    511
  • Lastpage
    516
  • Abstract
    To drive a single joint of rehabilitation robotic arm, we propose a new PM-TS actuator comprising a Pneumatic Muscle (PM) and a Torsion Spring (TS). Unlike the traditional agonist/antagonist PM actuator, the PM is arranged in appropriate place as agonist and the torsion spring provides opposing torque as antagonist in the proposed actuator. The 1-DOF and 2-DOF rehabilitation robotic arm models are derived considering the PM-TS dynamic model. To realize a high-accurate trajectory tracking control of the robotic arms, an intelligent PID controller based on an Echo State Neural Network (ESN) is proposed, where the ESN state is updated by the online Recursive Least Square (RLS) algorithm. Simulation results demonstrate the validity of PM-TS actuators. The performance of RLS-ESN based PID controller is found more satisfactory than conventional PID controller in our study.
  • Keywords
    least squares approximations; manipulators; medical robotics; neurocontrollers; patient rehabilitation; pneumatic actuators; position control; recursive estimation; springs (mechanical); three-term control; torque control; torsion; PM-TS actuators; RLS algorithm; RLS-ESN; antagonist PM actuator; echo state neural network; intelligent PID controller; online recursive least square algorithm; opposing torque; pneumatic muscle; rehabilitation robotic arms; torsion spring; trajectory tracking control; Recurrent neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Modelling, Identification and Control (ICMIC), The 2010 International Conference on
  • Conference_Location
    Okayama
  • Print_ISBN
    978-1-4244-8381-5
  • Electronic_ISBN
    978-0-9555293-3-7
  • Type

    conf

  • Filename
    5553510