Temperature compensation for six-dimension force/torque sensor based on Radial Basis Function Neural Network

被引：0
|
作者：
Sun, Yongjun [1 ]
Liu, Yiwei [1 ]
Liu, Hong [1 ]
机构：
[1] Harbin Inst Technol, State Key Lab Robot & Syst, Dept Mechatron Engn, Harbin 15001, Heilongjiang Pr, Peoples R China
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) | 2014年
关键词：
six-dimension force/torque sensor; temperature drift; Radial Basis Function Neural Network; temperature compensation; PRESSURE SENSOR;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Not only output of the six-dimension force/torque sensor changes with force or torque, but also be susceptible to ambient temperature, thus limiting measurement accuracy of the sensor. In order to overcome the above drawbacks of six-dimension force/torque sensor, this paper proposes a temperature compensation method based on Radial Basis Function (RBF) Neural Network. Compared with the conventional least squares method (LSM), RBF Neural Network has advantage obviously in compensating temperature drift for output nonlinear problems. Therefore, this method can eliminate the influence temperature drift of the sensor effectively. Examples show that the six-dimension force/torque sensor compensated by RBF has higher measurement precision and temperature stability.
引用
收藏
页码：789 / 794
页数：6
相关论文
共 50 条
  • [31] Risk Warning Model Based on Radial Basis Function Neural Network
    Zhao, Ping
    Liu, Xiaojun
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 5817 - 5820
  • [32] Camera calibration method based on radial basis function neural network
    Liu Ding
    Yan Zhen-jie
    Liang Yan-ming
    Yang Yan-xi
    Proceedings of 2004 Chinese Control and Decision Conference, 2004, : 459 - 461
  • [33] Individual Pitch Control Based on Radial Basis Function Neural Network
    Han, Bing
    Zhou, Lawu
    Zhang, Zhiwen
    Tian, Meng
    Deng, Ningfeng
    PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND AUTOMATIC CONTROL, 2016, 367 : 1 - 8
  • [34] Radial basis function neural network based on ant colony optimization
    Man Chun-tao
    Li Xiao-xia
    Zhang Li-yong
    CIS WORKSHOPS 2007: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY WORKSHOPS, 2007, : 59 - +
  • [35] Risk warning model based on radial basis function neural network
    Zhao, Ping
    Yan, Haitao
    2006 XI'AN INTERNATIONAL CONFERENCE OF ARCHITECTURE AND TECHNOLOGY, PROCEEDINGS: ARCHITECTURE IN HARMONY, 2006, : 1093 - 1096
  • [36] Radial basis function based probabilistic neural network in arrhythmia classification
    Chen, Chongyang
    Cai, Ping
    Shi, Wenkang
    Guo, nengwu
    Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 2000, 34 (11): : 1475 - 1477
  • [37] Realization of inverse system based on radial basis function neural network
    Chen, Xiaohong
    Hou, Chunhai
    Qian, Jixin
    Kongzhi yu Juece/Control and Decision, 1998, 13 (02): : 146 - 150
  • [38] Research on mixture neural network model based on radial basis function
    School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
    Wuhan Ligong Daxue Xuebao, 2007, 2 (122-125+142):
  • [39] Aeroheating agent model based on radial basis function neural network
    Zhang Z.
    Gao T.
    Zhang L.
    Tuo S.
    Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica, 2021, 42 (04):
  • [40] Project investment estimation based on radial basis function neural network
    Liu, Shu-Xian
    Duan, Xiao-Mu
    Yang, Jian-Ping
    Liaoning Gongcheng Jishu Daxue Xuebao (Ziran Kexue Ban)/Journal of Liaoning Technical University (Natural Science Edition), 2005, 24 (02): : 208 - 210
12345