文献类型：期刊文献

中文题名：Arm motion control model based on central pattern generator

英文题名：Arm motion control model based on central pattern generator

作者：Zhigang ZHENG[1];Rubin WANG[1]

机构：[1]Institute for Cognitive Neurodynamics,East China University of Science and Technology,Shanghai 200237,China

年份：2017

卷号：38

期号：9

起止页码：1247

中文期刊名：Applied Mathematics and Mechanics(English Edition)

外文期刊名：应用数学和力学（英文版）

收录：Scopus;CSCD:【CSCD2017_2018】；

基金：supported by the National Natural Science Foundation of China(Nos.11232005 and11472104)

语种：英文

中文关键词：central pattern generator （CPG）;arm motion;joint angle;hand angle,crank rotation experiment

外文关键词：central pattern generator （CPG）, arm motion, joint angle, hand angle,crank rotation experiment

摘要：According to the theory of Matsuoka neural oscillators and with the con- sideration 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 pro- posed 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.
According to the theory of Matsuoka neural oscillators and with the con- sideration 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 pro- posed 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.