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
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