| 面向带电作业的手臂末端输出力评估方法 |
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| 引用本文： | 林子,熊鹏文,何孔飞,张发辉.面向带电作业的手臂末端输出力评估方法[J].南京气象学院学报,2019,11(2):186-191. |
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| 作者姓名： | 林子 熊鹏文 何孔飞 张发辉 |
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| 作者单位： | 江西省电力科学研究院, 南昌, 330096,南昌大学 信息工程学院, 南昌, 330031,南昌大学 信息工程学院, 南昌, 330031,南昌大学 信息工程学院, 南昌, 330031 |
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| 基金项目： | 国家自然科学基金（61663027）；中国博士后科学基金（2018M642137）；江西省自然科学基金（20181BAB211019）；江西省教育厅科学技术研究项目（GJJ160212）；江苏省博士后科研资助计划（2018K024A） |
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| 摘 要： | 为了解决带电作业时手臂末端输出力的准确控制，提出一种基于表面肌电信号（sEMG信号）和支持向量机回归（SVR）实现对手臂末端施力的评估方法.通过手握机械手臂末端的手柄，做往复推拉运动，记录此时手柄处的力传感器的数据F，同时利用3组肌电信号传感器同步采集手臂的肌电信号.将肌电信号提取特征后，与力F组合成样本集合S，在样本集合中随机抽取50%的样本数据作为训练集，分别训练BP神经网络、GRNN神经网络以及SVR神经网络.最后用训练好的神经网络对整个样本集中的力F进行预测，并用均方根误差和相关系数评估模型的预测效果.结果显示，SVR神经网络的预测效果较好，其均方根误差为3.074 0，相关系数为0.951 7.
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| 关 键 词： | 带电作业 表面肌电信号 支持向量机 特征提取 回归预测 |
| 收稿时间： | 2019/2/25 0:00:00 |
Evaluation of output force at the arm end based on sEMG |
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| LIN Zi,XIONG Pengwen,HE Kongfei and ZHANG Fahui.Evaluation of output force at the arm end based on sEMG[J].Journal of Nanjing Institute of Meteorology,2019,11(2):186-191. |
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| Authors: | LIN Zi XIONG Pengwen HE Kongfei and ZHANG Fahui |
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| Institution: | Jiangxi Power Research Institute, Nanchang 330096,School of Information Nanchang on Engineering, Nanchang University, Nanchang 330031,School of Information Nanchang on Engineering, Nanchang University, Nanchang 330031 and School of Information Nanchang on Engineering, Nanchang University, Nanchang 330031 |
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| Abstract: | To facilitate accurate control of the output force at the arm end during live working,a prediction method for arm end force based on surface electromyography (sEMG) and support vector machine regression (SVR) is proposed.By forcing the handle at the end of the manipulator to provide the reciprocating push and pull movement,the data of the force sensor at handle Fare recorded.Simultaneously,the EMG signals of the arm are synchronously collected by three sets of EMG sensors.The feature extraction from the EMG signal and F are combined as sample set S inthe sample collection.Half of the sample data is randomly selected as the training set for different machine learning methods,and trained using the back propagation,generalized regression,and SVR neural networks.Finally,the trained neural network is used to predict force F in the whole sample set,and the prediction effect of the model is obtained by means of the root mean square error (RMSE) and correlation coefficient.The results show that the prediction effect of the SVR neural network is better,the RMSE is 3.074 0,and the correlation coefficient R is 0.951 7. |
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| Keywords: | live working surface electromyography signal support vector machines feature extraction regression prediction |
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