Hysteresis Compensation of 3D Printed Sensors Using a Power Law Model for Various Input Signals

Martijn  Schouten; Dimitrios  Kosmas; Gijs Krijnen

doi:10.1109/SENSORS47125.2020.9278942

Hysteresis Compensation of 3D Printed Sensors Using a Power Law Model for Various Input Signals

Martijn Schouten
, Dimitrios Kosmas
, Gijs Krijnen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

5 Citations (Scopus)

275 Downloads (Pure)

Abstract

We calculated the stability criterion for the modified power law model and subsequently adjust the model such that it’s stability can be guaranteed. We applied both a sinusoid, a chirp, an exponentially decaying sine and bandwidth limited noise position excitation to a 3D printed symmetric piezoresistive cantilever and measure the differential response. A modified power law model is fitted directly to the inverse of the sensor data, to directly obtain a compensator for the sensor. The result was a stable compensator that reduced the hysteresis of the 3D printed sensor.

Original language	English
Title of host publication	2020 IEEE Sensors
Publisher	IEEE
ISBN (Electronic)	978-1-7281-6801-2
ISBN (Print)	978-1-7281-6802-9
DOIs	https://doi.org/10.1109/SENSORS47125.2020.9278942
Publication status	Published - 9 Dec 2020
Event	IEEE Sensors 2020 - Virtual Event, Virtual, Rotterdam, Netherlands Duration: 25 Oct 2020 → 28 Oct 2020 https://2020.ieee-sensorsconference.org/

Conference

Conference	IEEE Sensors 2020
Country/Territory	Netherlands
City	Virtual, Rotterdam
Period	25/10/20 → 28/10/20
Internet address	https://2020.ieee-sensorsconference.org/

Keywords

3D printing
Hysteresis
Compensation
Flexible
Soft
Tactile sensor
Power Law
Non-linear
Stability
Recurrent
Shallow
Neural network

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10.1109/SENSORS47125.2020.9278942

ArticleAccepted author version, 1.05 MBLicence: CC BY

Cite this

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title = "Hysteresis Compensation of 3D Printed Sensors Using a Power Law Model for Various Input Signals",

abstract = "We calculated the stability criterion for the modified power law model and subsequently adjust the model such that it{\textquoteright}s stability can be guaranteed. We applied both a sinusoid, a chirp, an exponentially decaying sine and bandwidth limited noise position excitation to a 3D printed symmetric piezoresistive cantilever and measure the differential response. A modified power law model is fitted directly to the inverse of the sensor data, to directly obtain a compensator for the sensor. The result was a stable compensator that reduced the hysteresis of the 3D printed sensor.",

keywords = "3D printing, Hysteresis, Compensation, Flexible, Soft, Tactile sensor, Power Law, Non-linear, Stability, Recurrent, Shallow, Neural network",

author = "Martijn Schouten and Dimitrios Kosmas and Gijs Krijnen",

note = "Funding Information: This work was developed within the Wearable Robotics programme, funded by the Dutch Research Council (NWO). Publisher Copyright: {\textcopyright} 2020 IEEE.; IEEE Sensors 2020 ; Conference date: 25-10-2020 Through 28-10-2020",

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Y1 - 2020/12/9

N2 - We calculated the stability criterion for the modified power law model and subsequently adjust the model such that it’s stability can be guaranteed. We applied both a sinusoid, a chirp, an exponentially decaying sine and bandwidth limited noise position excitation to a 3D printed symmetric piezoresistive cantilever and measure the differential response. A modified power law model is fitted directly to the inverse of the sensor data, to directly obtain a compensator for the sensor. The result was a stable compensator that reduced the hysteresis of the 3D printed sensor.

AB - We calculated the stability criterion for the modified power law model and subsequently adjust the model such that it’s stability can be guaranteed. We applied both a sinusoid, a chirp, an exponentially decaying sine and bandwidth limited noise position excitation to a 3D printed symmetric piezoresistive cantilever and measure the differential response. A modified power law model is fitted directly to the inverse of the sensor data, to directly obtain a compensator for the sensor. The result was a stable compensator that reduced the hysteresis of the 3D printed sensor.

KW - 3D printing

KW - Hysteresis

KW - Compensation

KW - Flexible

KW - Soft

KW - Tactile sensor

KW - Power Law

KW - Non-linear

KW - Stability

KW - Recurrent

KW - Shallow

KW - Neural network

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DO - 10.1109/SENSORS47125.2020.9278942

M3 - Conference contribution

SN - 978-1-7281-6802-9

BT - 2020 IEEE Sensors

PB - IEEE

T2 - IEEE Sensors 2020

Y2 - 25 October 2020 through 28 October 2020

ER -

Hysteresis Compensation of 3D Printed Sensors Using a Power Law Model for Various Input Signals

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