Neural Network Based Posture Control of a Human Arm Model in the Sagittal Plane

  • Shan Liu
  • Yongji Wang
  • Jian Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


In this paper posture control of a human arm in the sagittal plane is investigated by means of model simulations. The arm is modeled by a nonlinear neuromusculoskeletal model with two degrees of freedom and six muscles. A multilayer perceptron network is used in this paper, and effectively adapted by Levenberg-Marquardt training algorithm. The duration of next movement is regulated according as current feedback states. Simulation Results indicate that this method can maintain two joints at different location in allowable bound. The control scheme provides novel insight into neural prosthesis control and robotic control.


Posture Control Feedforward Control Elbow Angle Motor Control System Passive Torque 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shan Liu
    • 1
  • Yongji Wang
    • 1
  • Jian Huang
    • 1
  1. 1.Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhanChina

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