Volume 67 Issue 12
Dec.  2023
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SONG Panfeng. A Review of Dynamic Force Decoupling and Calibration Techniques[J]. Metrology Science and Technology, 2023, 67(12): 40-46, 66. doi: 10.12338/j.issn.2096-9015.2023.0326
Citation: SONG Panfeng. A Review of Dynamic Force Decoupling and Calibration Techniques[J]. Metrology Science and Technology, 2023, 67(12): 40-46, 66. doi: 10.12338/j.issn.2096-9015.2023.0326

A Review of Dynamic Force Decoupling and Calibration Techniques

doi: 10.12338/j.issn.2096-9015.2023.0326
  • Received Date: 2023-12-01
  • Accepted Date: 2023-12-05
  • Rev Recd Date: 2023-12-08
  • Available Online: 2023-12-15
  • Publish Date: 2023-12-18
  • Dynamic force, a key measurement parameter in fields such as aerospace, material science, automobile manufacturing, and weapon efficacy, refers to force that varies over time. Ideally, dynamic force measurements should be unaffected by other sensor outputs, focusing solely on the test channel's directional output. Nevertheless, limitations in processing and installation technologies often result in inadvertent coupling of signals from other directions, leading to dynamic coupling. Accurate dynamic force measurements hinge on identifying the interdimensional coupling relationships among forces. Additionally, sensor calibration prior to testing is essential for enhancing measurement accuracy. Currently, the prevalent practice of using static calibration for dynamic force measurement often results in significant errors. Hence, the study and application of dynamic force decoupling and calibration methods are of paramount importance. This paper synthesizes various literature, summarizing common methods of dynamic force decoupling and calibration, comparing their strengths and weaknesses, and providing future perspectives.
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  • [1]
    李洋. 大尺寸模型气动力多点悬挂测量研究[D]. 大连: 大连理工大学, 2021.
    [2]
    张军, 王郁赫, 任宗金, 等. 考虑角度偏差的压电三维力传感器标定[J]. 光学精密工程, 2023, 31(17): 2546-2554.
    [3]
    徐家琪, 龙建勇, 孙炜, 等. 基于RF-GA的六维力传感器解耦方法[J]. 测控技术, 2020, 39(5): 28-35,68.
    [4]
    Sun Y J, Liu Y W, Liu H. Analysis calibration system error of six-dimension force/torque sensor for space robot [C]. IEEE. International Conference on Mechatronics and Automation: Tianjin, 2014.
    [5]
    Song A G, Wu J, Qin G, et al. A novel self-decoupled four degree-of-freedom wrist force/torque sensor[J]. Measurement, 2007, 40: 883-891. doi: 10.1016/j.measurement.2006.11.018
    [6]
    张见冈, 张军, 钱敏, 等. 压电石英六维力传感器的解耦研究[J]. 压电与声光, 2010, 32(3): 396-398.
    [7]
    马俊青, 宋爱国, 吴涓. 三维力传感器静态解耦算法的研究与应用[J]. 计量学报, 2011, 32(6): 517-521.
    [8]
    姚建涛, 侯雨雷, 姚建业, 等. 大量程预紧式六维力/力矩传感器及静态标定研究[J]. 仪器仪表学报, 2009, 30(6): 1233-1239.
    [9]
    尹肖, 王宇, 杨军. 动态力校准技术评述[J]. 计测技术, 2015, 35(2): 6-10.
    [10]
    商佳尚, 王宇. 动态力校准中需要规范的若干问题[J]. 计测技术, 2014, 34(2): 1-5.
    [11]
    闫瑞, 骆昕, 赵强, 等. 基于LabVIEW的冲击法动态力传感器校准装置[J]. 计量科学与技术, 2021, 65(10): 54-57,62.
    [12]
    张多利, 金龙学, 吴月明. 小力值拉力计检定装置设计[J]. 中国检验检测, 2023, 31(4): 31-32,102.
    [13]
    段武斌. 大吨位六分力测量系统原位标定及数据处理方法研究[D]. 南京: 南京理工大学, 2009.
    [14]
    李彦刚. 压电多维力传感器的静动态标定系统研究[D]. 重庆: 重庆大学, 2010.
    [15]
    吕江山. 轨姿控火箭发动机多维推力测量研究[D]. 大连: 大连理工大学, 2022.
    [16]
    Park Yon-Kyu, Kumme Rolf, Kang Dae-Im. Dynamic investigation of a three-component force-moment sensor[J]. Measurement Science and Technology, 2002(13): 654-659.
    [17]
    刘奕辰, 朱熀秋, 杨泽斌. 无轴承电机神经网络逆系统解耦控制关键技术发展综述[J/OL]. [2023-12-01]. http://kns.cnki.net/kcms/detail/41.1148.TH.20230822.1009.002.html.
    [18]
    唐佳豪, 闻新, 王宁, 等. 基于神经网络的控制力矩陀螺自抗扰解耦控制[J]. 兵工自动化, 2023, 42(2): 35-41.
    [19]
    张浩. 六极径向混合磁轴承优化设计及自抗扰解耦控制研究[D]. 镇江: 江苏大学, 2022.
    [20]
    蒋毅恒, 李霄飞. 对角优势的分析与综述[J]. 电力情报, 2000(3): 8-11.
    [21]
    孟晨, 蒋继乐, 郭斌, 等. 基于神经网络的扭矩传感器稳定性分析预测[J]. 计量科学与技术, 2022, 66(5): 8-14,68.
    [22]
    包子阳, 余继周. 智能优化算法及其 MATLAB 实例[M]. 第 1 版. 北京: 电子工业出版社, 2016: 170-173.
    [23]
    李阳, 张建, 程序. 神经网络和滞后变量回归的车载终端误差修正[J]. 计量科学与技术, 2023, 67(8): 48-53.
    [24]
    周爱国, 曾智杰, 乌建中, 等. 风电叶片多点静力测试神经网络PID解耦控制[J]. 测控技术, 2021, 40(3): 123-129.
    [25]
    王清华, 郭伟国, 徐丰, 等. 基于Hopkinson杆和人工神经网络的三轴冲击力传感器同步解耦标定方法[J]. 爆炸与冲击, 2022, 42(10): 87-98.
    [26]
    张景柱. 特种六分力传感器设计原理研究[D]. 南京: 南京理工大学, 2008.
    [27]
    陈宏伟, 卢文科, 左锋. 基于SSA-LSSVM模型的差动电感式位移传感器的温度补偿[J]. 仪表技术, 2023(6): 69-73.
    [28]
    易智文. 基于优化经验模态分解和最小二乘支持向量机的边坡位移预测[J]. 江西水利科技, 2023, 49(5): 327-333.
    [29]
    李华, 朱一民, 马海军, 等. 基于最小二乘支持向量机的小电流接地系统早期故障识别算法研究[J]. 电气工程学报, 2023, 18(3): 297-306.
    [30]
    盛文娟, 娄海涛, 彭刚定. 基于最小二乘支持向量机和多参考光栅的可调谐滤波器解调误差动态补偿[J]. 光学学报, 2023, 43(7): 35-45.
    [31]
    Su J , Zhang Y N . Application of BP neural network optimization algorithm based on genetic algorithm in credit risk early-warning of commercial bank[C]. 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, 2017.
    [32]
    Xie X L , Gao F , Huang X Y , et al. Numerical optimization of flow noises for mufflers based on the improved BP neural network[J]. Journal of Vibroengineering, 2016, 18(4): 1.
    [33]
    秦媛. 粒子群算法改进及其应用研究[D]. 南京: 南京邮电大学, 2018.
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