High-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling

This paper proposes a feedforward and feedback combined hysteresis compensation method for a piezoelectric actuator (PEA) based on the multi-layer feedforward neural network (MFNN) inverse model. Under the scheme of direct inverse modeling, the MFNN is utilized as the feedforward hysteresis compensa...

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Autores principales:	Yanding Qin, Yunpeng Zhang, Heng Duan, Jianda Han
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	piezoelectric actuator hysteresis compensation neural network inverse modeling rate-dependent Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/08d3fce178704053a7e2a4dac195237d
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id	oai:doaj.org-article:08d3fce178704053a7e2a4dac195237d
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spelling	oai:doaj.org-article:08d3fce178704053a7e2a4dac195237d2021-11-25T18:23:08ZHigh-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling10.3390/mi121113252072-666Xhttps://doaj.org/article/08d3fce178704053a7e2a4dac195237d2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-666X/12/11/1325https://doaj.org/toc/2072-666XThis paper proposes a feedforward and feedback combined hysteresis compensation method for a piezoelectric actuator (PEA) based on the multi-layer feedforward neural network (MFNN) inverse model. Under the scheme of direct inverse modeling, the MFNN is utilized as the feedforward hysteresis compensator, which can be directly identified from the measurements. The high modeling accuracy and high robustness of the MFNN help to increase the bandwidth of the closed-loop system. Experiments are conducted on a commercial PEA so as to verify the effectiveness of the proposed method. The superimposition of two sinusoidal signals is found to be efficient for the training of the MFNN. Closed-loop trajectory tracking experiments demonstrate that the bandwidth can be increased up to 1000 Hz and the maximum deviation can be maintained closed to the noise level. Meanwhile, there are only two parameters to be tuned in the proposed method, which guarantees ease of use for the inexperienced users. The proposed method successfully realizes high-precision hysteresis compensation performance across a wider frequency range.Yanding QinYunpeng ZhangHeng DuanJianda HanMDPI AGarticlepiezoelectric actuatorhysteresis compensationneural networkinverse modelingrate-dependentMechanical engineering and machineryTJ1-1570ENMicromachines, Vol 12, Iss 1325, p 1325 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	piezoelectric actuator hysteresis compensation neural network inverse modeling rate-dependent Mechanical engineering and machinery TJ1-1570
spellingShingle	piezoelectric actuator hysteresis compensation neural network inverse modeling rate-dependent Mechanical engineering and machinery TJ1-1570 Yanding Qin Yunpeng Zhang Heng Duan Jianda Han High-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling
description	This paper proposes a feedforward and feedback combined hysteresis compensation method for a piezoelectric actuator (PEA) based on the multi-layer feedforward neural network (MFNN) inverse model. Under the scheme of direct inverse modeling, the MFNN is utilized as the feedforward hysteresis compensator, which can be directly identified from the measurements. The high modeling accuracy and high robustness of the MFNN help to increase the bandwidth of the closed-loop system. Experiments are conducted on a commercial PEA so as to verify the effectiveness of the proposed method. The superimposition of two sinusoidal signals is found to be efficient for the training of the MFNN. Closed-loop trajectory tracking experiments demonstrate that the bandwidth can be increased up to 1000 Hz and the maximum deviation can be maintained closed to the noise level. Meanwhile, there are only two parameters to be tuned in the proposed method, which guarantees ease of use for the inexperienced users. The proposed method successfully realizes high-precision hysteresis compensation performance across a wider frequency range.
format	article
author	Yanding Qin Yunpeng Zhang Heng Duan Jianda Han
author_facet	Yanding Qin Yunpeng Zhang Heng Duan Jianda Han
author_sort	Yanding Qin
title	High-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling
title_short	High-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling
title_full	High-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling
title_fullStr	High-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling
title_full_unstemmed	High-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling
title_sort	high-bandwidth hysteresis compensation of piezoelectric actuators via multilayer feedforward neural network based inverse hysteresis modeling
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/08d3fce178704053a7e2a4dac195237d
work_keys_str_mv	AT yandingqin highbandwidthhysteresiscompensationofpiezoelectricactuatorsviamultilayerfeedforwardneuralnetworkbasedinversehysteresismodeling AT yunpengzhang highbandwidthhysteresiscompensationofpiezoelectricactuatorsviamultilayerfeedforwardneuralnetworkbasedinversehysteresismodeling AT hengduan highbandwidthhysteresiscompensationofpiezoelectricactuatorsviamultilayerfeedforwardneuralnetworkbasedinversehysteresismodeling AT jiandahan highbandwidthhysteresiscompensationofpiezoelectricactuatorsviamultilayerfeedforwardneuralnetworkbasedinversehysteresismodeling
_version_	1718411239729659904

High-Bandwidth Hysteresis Compensation of Piezoelectric Actuators via Multilayer Feedforward Neural Network Based Inverse Hysteresis Modeling

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