基于EtherCAT总线的六维力传感器在线解耦技术

doi:10.3785/j.issn.1008-973X.2023.10.013

浙江大学学报(工学版)

2023, Vol. 57

Issue (10): 2042-2050 DOI: 10.3785/j.issn.1008-973X.2023.10.013

机械工程、能源工程

基于EtherCAT总线的六维力传感器在线解耦技术

查浩1(

),费少华2,傅云3,吕震4,朱伟东2,*(

)

1. 浙江大学工程师学院，浙江杭州 310015
2. 浙江大学机械工程学院，浙江杭州 310027
3. 浙江西子势必锐航空工业有限公司，浙江杭州 311228
4. 浙大城市学院，浙江杭州 310015

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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摘要：

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

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.

Key words: EtherCAT six-dimensional force sensor genetic algorithm neural network online decoupling

收稿日期: 2022-12-20 出版日期: 2023-10-18

CLC:

TP 212.6

基金资助: 浙江省“尖兵”“领雁”研发攻关计划资助项目（2022C01134）

通讯作者: 朱伟东 E-mail: zhahao@zju.edu.cn;wdzhu@zju.edu.cn

作者简介: 查浩（1997—），男，硕士生，从事自动化装配技术及系统研究. orcid.org/0000-0001-8191-2391. E-mail： zhahao@zju.edu.cn

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引用本文:

查浩,费少华,傅云,吕震,朱伟东. 基于EtherCAT总线的六维力传感器在线解耦技术[J]. 浙江大学学报(工学版), 2023, 57(10): 2042-2050.

Hao ZHA,Shao-hua FEI,Yun FU,Zhen LV,Wei-dong ZHU. Online decoupling technology of six-dimensional force sensor based on EtherCAT bus. Journal of ZheJiang University (Engineering Science), 2023, 57(10): 2042-2050.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.10.013 或 https://www.zjujournals.com/eng/CN/Y2023/V57/I10/2042

图 1 六维力传感器的内部结构图

图 2 六维力传感器数据采集模块的硬件组成图

图 3 从站的原理框图

图 4 六维力传感器数据采集模块的程序初始化流程图

图 5 过程数据对象的分配与映射

图 6 过程数据的采集流程

图 7 神经网络的结构图

图 8 遗传算法优化的神经网络算法

图 9 在线解耦的流程图

图 10 六维力传感器数据采集实验的现场图

表 1 单维加载实验的数据相对误差

表 2 单维加载实验的数据波动特性

图 11 单维加载实验的线性度特性图

图 12 六维力传感器的六维加载实验

图 13 六维加载实验预测力的相对误差图

表 3 最小二乘法解耦矩阵的计算误差

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