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作 者:查浩 费少华[2] 傅云 吕震 朱伟东[2] ZHA Hao;FEI Shao-hua;FU Yun;LV Zhen;ZHU Wei-dong(Polytechnic Institute,Zhejiang University,Hangzhou 310015,China;College of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China;Zhejiang Xizi Spirit Aviation Industry Ltd.,Hangzhou 311228,China;Zhejiang University City College,Hangzhou 310015,China)
机构地区:[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
出 处:《浙江大学学报(工学版)》2023年第10期2042-2050,共9页Journal of Zhejiang University:Engineering Science
基 金:浙江省“尖兵”“领雁”研发攻关计划资助项目(2022C01134).
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%.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.
关 键 词:ETHERCAT 六维力传感器 遗传算法 神经网络 在线解耦
分 类 号:TP212.6[自动化与计算机技术—检测技术与自动化装置]
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