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Sensors (Basel). 2019 Sep 30;19(19). pii: E4259. doi: 10.3390/s19194259.

Hysteresis Compensation in Force/Torque Sensors Using Time Series Information.

Author information

1
Graduate School of Science and Engineering, Saitama University, Sakura-ku, Saitama City, Saitama 338-8570, Japan. koiryu1221@gmail.com.
2
Department of Intelligent Interaction Technologies, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan. sakaino@iit.tsukuba.ac.jp.
3
Graduate School of Science and Engineering, Saitama University, Sakura-ku, Saitama City, Saitama 338-8570, Japan. tsuji@ees.saitama-u.ac.jp.

Abstract

The purpose of this study is to compensate for the hysteresis in a six-axis force sensor using signal processing, thereby achieving high-precision force sensing. Although mathematical models of hysteresis exist, many of these are one-axis models and the modeling is difficult if they are expanded to multiple axes. Therefore, this study attempts to resolve this problem through machine learning. Since hysteresis is dependent on the previous history, this study investigates the effect of using time series information in machine learning. Experimental results indicate that the performance is improved by including time series information in the linear regression process generally utilized to calibrate six-axis force sensors.

KEYWORDS:

force sensing; force sensor; hysteresis compensation; linear regression; tactile sensing

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