Arm motion control model based on central pattern generator

doi:10.1007/s10483-017-2240-8

Abstract

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

2010 MSC Number:

92B20

Zhigang ZHENG, Rubin WANG. Arm motion control model based on central pattern generator. Applied Mathematics and Mechanics (English Edition), 2017, 38(9): 1247-1256.

TrendMD

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