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| Online decoupling technology of six-dimensional force sensor based on EtherCAT bus |
Hao ZHA1( ),Shao-hua FEI2,Yun FU3,Zhen LV4,Wei-dong ZHU2,*() |
1. Polytechnic Institute, Zhejiang University, Hangzhou 310015, China
2. College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
3. Zhejiang Xizi Spirit Aviation Industry Ltd., Hangzhou 311228, China
4. Zhejiang University City College, Hangzhou 310015, China |
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Abstract A six-dimensional force sensor data acquisition module based on EtherCAT bus was designed, overcoming the drawbacks of traditional modules, like large size, low accuracy, and absence of data processing. The module had small size, high precision, high real-time, fast acquisition speed and integration of acquisition and processing. A neural network optimized by genetic algorithm, was developed for offline training of experimental data, so as to decrease the inter-dimensional coupling of six-dimensional force sensor. Multiple randomization was used to enhance the generalization performance of the algorithm, and the trained parameters were transplanted to the microcontroller for the online decoupling. Through single-dimensional and six-dimensional force sensor loading experiments, the result indicated that the data acquisition and processing could be achieved by module within 1 ms. Relative error was less than 0.85% after decoupling, the accuracy improved by 37.1% compared to the least squares decoupling matrix calculation.
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Received: 20 December 2022
Published: 18 October 2023
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| Fund: 浙江省“尖兵”“领雁”研发攻关计划资助项目（2022C01134） |
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Corresponding Authors:
Wei-dong ZHU
E-mail: zhahao@zju.edu.cn;wdzhu@zju.edu.cn
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基于EtherCAT总线的六维力传感器在线解耦技术
为了克服传统六维力传感器数据采集模块体积大、精度低、缺乏数据处理功能的弊端，设计了一种体积小、精度高、实时性高、采集速度快、采集处理一体化的基于EtherCAT总线的六维力传感器数据采集模块. 为了减小六维力传感器的维间耦合，开发了遗传算法优化的神经网络来对实验数据进行离线训练. 利用多重随机操作来提高算法的泛化性能，并将训练好的参数移植到微控制器中进行在线解耦. 通过单维和六维力传感器加载实验，表明模块可在1 ms之内完成数据的采集及处理；解耦后相对误差在0.85%以下，相对于最小二乘法解耦矩阵计算得到的精度提高了37.1%.
关键词：
EtherCAT,
六维力传感器,
遗传算法,
神经网络,
在线解耦
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