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Arm motion control model based on central pattern generator

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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.

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Authors and Affiliations

  1. Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, 200237, China

    Zhigang Zheng & Rubin Wang

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  1. Zhigang Zheng
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  2. Rubin Wang
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Correspondence to Rubin Wang.

Additional information

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

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Zheng, Z., Wang, R. Arm motion control model based on central pattern generator. Appl. Math. Mech.-Engl. Ed. 38, 1247–1256 (2017). https://doi.org/10.1007/s10483-017-2240-8

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  • DOI: https://doi.org/10.1007/s10483-017-2240-8

Key words

Chinese Library Classification

2010 Mathematics Subject Classification

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