Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor

To measure three-dimensional (3D) forces efficiently and improve the sensitivity of tactile sensors, a novel piezoelectric tactile sensor with a “sandwich” structure is proposed in this paper. An array of circular truncated cone-shaped sensitive units made of polyvinylidene fluoride (PVDF) is sandwi...

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Autores principales:	Feilu Wang, Rungen Ye, Yang Song, Yufeng Chen, Yanan Jiang, Shanna Lv, Mingkun Li
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/ec84a441ea224aeba9e1826cdb2845c8
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spelling	oai:doaj.org-article:ec84a441ea224aeba9e1826cdb2845c82021-11-15T01:19:38ZModeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor1687-726810.1155/2021/3667833https://doaj.org/article/ec84a441ea224aeba9e1826cdb2845c82021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3667833https://doaj.org/toc/1687-7268To measure three-dimensional (3D) forces efficiently and improve the sensitivity of tactile sensors, a novel piezoelectric tactile sensor with a “sandwich” structure is proposed in this paper. An array of circular truncated cone-shaped sensitive units made of polyvinylidene fluoride (PVDF) is sandwiched between two flexible substrates of polydimethylsiloxane (PDMS). Based on the piezoelectric properties of the PVD F sensitive units, finite element modelling and analysis are carried out for the sensor. The relation between the force and the voltage of the sensitive unit is obtained, and a tactile perception model is established. The model can distinguish the sliding direction and identify the material of the slider loaded on the sensor. A backpropagation neural network (BPNN) algorithm is built to predict the 3D forces applied on the tactile sensor model, and the 3D forces are decoupled from the voltages of the sensitive units. The BPNN is further optimized by a genetic algorithm (GA) to improve the accuracy of the 3D force prediction, and fairly good prediction results are obtained. The experimental results show that the novel tactile sensor model can effectively predict the 3D forces, and the BPNN model optimized by the GA can predict the 3D forces with much higher precision, which also improves the intelligence of the sensor. All the prediction results indicate that the BPNN algorithm has very efficient performance in 3D force prediction for the piezoelectric tactile sensor.Feilu WangRungen YeYang SongYufeng ChenYanan JiangShanna LvMingkun LiHindawi LimitedarticleTechnology (General)T1-995ENJournal of Sensors, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995
spellingShingle	Technology (General) T1-995 Feilu Wang Rungen Ye Yang Song Yufeng Chen Yanan Jiang Shanna Lv Mingkun Li Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor
description	To measure three-dimensional (3D) forces efficiently and improve the sensitivity of tactile sensors, a novel piezoelectric tactile sensor with a “sandwich” structure is proposed in this paper. An array of circular truncated cone-shaped sensitive units made of polyvinylidene fluoride (PVDF) is sandwiched between two flexible substrates of polydimethylsiloxane (PDMS). Based on the piezoelectric properties of the PVD F sensitive units, finite element modelling and analysis are carried out for the sensor. The relation between the force and the voltage of the sensitive unit is obtained, and a tactile perception model is established. The model can distinguish the sliding direction and identify the material of the slider loaded on the sensor. A backpropagation neural network (BPNN) algorithm is built to predict the 3D forces applied on the tactile sensor model, and the 3D forces are decoupled from the voltages of the sensitive units. The BPNN is further optimized by a genetic algorithm (GA) to improve the accuracy of the 3D force prediction, and fairly good prediction results are obtained. The experimental results show that the novel tactile sensor model can effectively predict the 3D forces, and the BPNN model optimized by the GA can predict the 3D forces with much higher precision, which also improves the intelligence of the sensor. All the prediction results indicate that the BPNN algorithm has very efficient performance in 3D force prediction for the piezoelectric tactile sensor.
format	article
author	Feilu Wang Rungen Ye Yang Song Yufeng Chen Yanan Jiang Shanna Lv Mingkun Li
author_facet	Feilu Wang Rungen Ye Yang Song Yufeng Chen Yanan Jiang Shanna Lv Mingkun Li
author_sort	Feilu Wang
title	Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor
title_short	Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor
title_full	Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor
title_fullStr	Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor
title_full_unstemmed	Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor
title_sort	modeling analysis and 3d force prediction of a novel piezoelectric tactile sensor
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/ec84a441ea224aeba9e1826cdb2845c8
work_keys_str_mv	AT feiluwang modelinganalysisand3dforcepredictionofanovelpiezoelectrictactilesensor AT rungenye modelinganalysisand3dforcepredictionofanovelpiezoelectrictactilesensor AT yangsong modelinganalysisand3dforcepredictionofanovelpiezoelectrictactilesensor AT yufengchen modelinganalysisand3dforcepredictionofanovelpiezoelectrictactilesensor AT yananjiang modelinganalysisand3dforcepredictionofanovelpiezoelectrictactilesensor AT shannalv modelinganalysisand3dforcepredictionofanovelpiezoelectrictactilesensor AT mingkunli modelinganalysisand3dforcepredictionofanovelpiezoelectrictactilesensor
_version_	1718428956802154496

Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor

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