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Open AccessArticle

Hysteresis Compensation in Force/Torque Sensors Using Time Series Information

1
Graduate School of Science and Engineering, Saitama University, Sakura-ku, Saitama City, Saitama 338-8570, Japan
2
Department of Intelligent Interaction Technologies, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4259; https://doi.org/10.3390/s19194259
Received: 29 August 2019 / Revised: 27 September 2019 / Accepted: 27 September 2019 / Published: 30 September 2019
(This article belongs to the Special Issue Tactile Sensors for Robotic Applications)
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. View Full-Text
Keywords: force sensing; force sensor; tactile sensing; linear regression; hysteresis compensation force sensing; force sensor; tactile sensing; linear regression; hysteresis compensation
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Koike, R.; Sakaino, S.; Tsuji, T. Hysteresis Compensation in Force/Torque Sensors Using Time Series Information. Sensors 2019, 19, 4259.

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