Applied Mathematics and Mechanics (English Edition) ›› 2017, Vol. 38 ›› Issue (9): 1247-1256.doi: https://doi.org/10.1007/s10483-017-2240-8

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Arm motion control model based on central pattern generator

Zhigang ZHENG, Rubin WANG   

  1. Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai 200237, China
  • 收稿日期:2016-11-21 修回日期:2017-04-05 出版日期:2017-09-01 发布日期:2017-09-01
  • 通讯作者: Rubin WANG,E-mail:rbwang@163.com E-mail:rbwang@163.com
  • 基金资助:

    Project supported by the National Natural Science Foundation of China (Nos.11232005 and 11472104)

Arm motion control model based on central pattern generator

Zhigang ZHENG, Rubin WANG   

  1. Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai 200237, China
  • Received:2016-11-21 Revised:2017-04-05 Online:2017-09-01 Published:2017-09-01
  • Contact: Rubin WANG E-mail:rbwang@163.com
  • Supported by:

    Project supported by the National Natural Science Foundation of China (Nos.11232005 and 11472104)

摘要:

According to the theory of Matsuoka neural oscillators and with the consideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator (CPG) neural network consisting of six neurons is proposed to regulate the contraction of the upper arm muscles. To verify effectiveness of the proposed CPG network, an arm motion control model based on the CPG is established. By adjusting the CPG parameters, we obtain the neural responses of the network, the angles of joint and hand of the model with MATLAB. The simulation results agree with the results of crank rotation experiments designed by Ohta et al., showing that the arm motion control model based on a CPG network is reasonable and effective.

关键词: crank rotation experiment, nonlinear dynamical system, adjoint operator method, normal forms of order 3 and 4, degenerate bifurcation of codimension 3, universal unfolding, arm motion, hand angle, central pattern generator (CPG), joint angle

Abstract:

According to the theory of Matsuoka neural oscillators and with the consideration of the fact that the human upper arm mainly consists of six muscles, a new kind of central pattern generator (CPG) neural network consisting of six neurons is proposed to regulate the contraction of the upper arm muscles. To verify effectiveness of the proposed CPG network, an arm motion control model based on the CPG is established. By adjusting the CPG parameters, we obtain the neural responses of the network, the angles of joint and hand of the model with MATLAB. The simulation results agree with the results of crank rotation experiments designed by Ohta et al., showing that the arm motion control model based on a CPG network is reasonable and effective.

Key words: nonlinear dynamical system, adjoint operator method, normal forms of order 3 and 4, degenerate bifurcation of codimension 3, universal unfolding, joint angle, arm motion, central pattern generator (CPG), crank rotation experiment, hand angle

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