摘要
针对农田耕整载荷大、测量精度低等问题,在经典十字梁结构基础上,设计了一种辐梁式六维力传感器,可同时测量力和力矩,通过仿真方法对传感器结构进行了优化,确定了应变梁长、宽、高分别为9、10、6 mm;分析了传感器结构在载荷下的应变能力,确定了应变片贴片位置。对传感器开展了静态标定试验,基于标定数据采用改进型XGBoost(Extreme gradient boosting)机器学习网络对力信号进行解耦,并与常规网络进行比对。试验结果表明,改进型XGBoost模型在X、Y、Z方向力和力矩6种加载方式的测试集决定系数R^(2)_(P)分别达到0.9804、0.9418、0.9434、0.9868、0.9969、0.9822,预测效果较好,避免了陷入局部最优解。改进型XGBoost模型在六维加载力、力矩的R^(2)_(P)、测试集平均绝对误差(MAEP)均明显优于随机森林模型、传统多元线性回归,相较于传统多元线性回归方式,六维加载力、力矩的R^(2)_(P)分别提升22.57%、20.99%、23.32%、26.27%、26.05%、18.72%。基于机器学习的解耦算法可明显减少耦合误差的影响,提高传感器的测量精度,为农机优化提供了技术支撑。
Aiming at the problems of large plowing load and low measurement accuracy,a six dimensional force sensor of radial beam type was designed on the basis of classical cross beam structure,which could measure force and moment at the same time.The sensor structure was optimized by simulation method,and the dimension length,width and height of strain beam were determined to be 9 mm,10 mm and 6 mm,respectively.The strain capacity of the sensor structure under load was analyzed,and the position of the strain gauge patch was determined.Based on the calibration data,the improved XGBoost(extreme gradient boosting)machine learning network was used to decouple the force signal.The improved XGBoost model achieved R^(2)_(P)(determination coefficient of test set)of 0.9804,0.9418,0.9434,0.9868,0.9969,and 0.9822 in six loading modes of force and torque in X,Y and Z directions,respectively.The prediction performance was good,avoiding getting stuck in local optimal solutions.And then compared with the conventional network,the R^(2)_(P) and MAEP(average absolute error of test set)of the improved XGBoost model in the six dimensional force loading direction were significantly better than that of the random forest model and the traditional multiple linear regression.Compared with the traditional multiple linear regression method,the R^(2)_(P) of the six dimensional loading force/moment was increased by 22.57%,20.99%,23.32%,26.27%,26.05%and 18.72%,respectively.Machine learning based decoupling algorithms could significantly reduce the impact of coupling errors and improve the measurement accuracy of sensors and provide technical support for optimizing agricultural machinery.
作者
陈伟
张晓
袁栋
朱继平
陈小兵
曹光乔
CHEN Wei;ZHANG Xiao;YUAN Dong;ZHU Jiping;CHEN Xiaobing;CAO Guangqiao(Nanjing Institute of Agricultural Mechanization,Ministry of Agriculture and Rural Affairs,Nanjing 210014,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2024年第2期28-35,89,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2022YFD2301302-5)
财政部和农业农村部:国家现代农业产业技术体系项目(CARS-05)
西藏自治区重大科技专项(XZ202101ZD0004-04)。
关键词
农田耕整载荷
辐梁式传感器
结构优化
解耦算法
机器学习
farmland tillage load
radial beam sensor
structural optimization
decoupling algorithm
machine learning
