An adaptive weighted least square support vector regression for hysteresis in piezoelectric actuators

Mao, X; Wang, Y; Liu, X; Guo, Y

An adaptive weighted least square support vector regression for hysteresis in piezoelectric actuators

Mao, X Wang, Y Liu, X Guo, Y

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Publication Type:: Journal Article
Citation:: Sensors and Actuators, A: Physical, 2017, 263 pp. 423 - 429
Issue Date:: 2017-08-15

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Field	Value	Language
dc.contributor.author	Mao, X	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684	en_US
dc.date.available	2020-05-25T19:09:50Z
dc.date.issued	2017-08-15	en_US
dc.identifier.citation	Sensors and Actuators, A: Physical, 2017, 263 pp. 423 - 429	en_US
dc.identifier.issn	0924-4247	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120564
dc.description.abstract	© 2017 Elsevier B.V. To overcome the low positioning accuracy of piezoelectric actuators (PZAs) caused by the hysteresis nonlinearity, this paper proposes an adaptive weighted least squares support vector regression (AWLSSVR) to model the rate-dependent hysteresis of PZA. Firstly, the AWLSSVR hyperparameters are optimized by using particle swarm optimization. Then an adaptive weighting strategy is proposed to eliminate the effects of noises in the training dataset and reduce the sample size at the same time. Finally, the proposed approach is applied to predict the hysteresis of PZA. The results show that the proposed method is more accurate than other versions of least squares support vector regression for training samples with noises, and meanwhile reduces the sample size and speeds up calculation.	en_US
dc.relation.ispartof	Sensors and Actuators, A: Physical	en_US
dc.relation.isbasedon	10.1016/j.sna.2017.06.030	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Nanoscience & Nanotechnology	en_US
dc.title	An adaptive weighted least square support vector regression for hysteresis in piezoelectric actuators	en_US
dc.type	Journal Article
utslib.citation.volume	263	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0912 Materials Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	263	en_US

Abstract:

© 2017 Elsevier B.V. To overcome the low positioning accuracy of piezoelectric actuators (PZAs) caused by the hysteresis nonlinearity, this paper proposes an adaptive weighted least squares support vector regression (AWLSSVR) to model the rate-dependent hysteresis of PZA. Firstly, the AWLSSVR hyperparameters are optimized by using particle swarm optimization. Then an adaptive weighting strategy is proposed to eliminate the effects of noises in the training dataset and reduce the sample size at the same time. Finally, the proposed approach is applied to predict the hysteresis of PZA. The results show that the proposed method is more accurate than other versions of least squares support vector regression for training samples with noises, and meanwhile reduces the sample size and speeds up calculation.

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http://hdl.handle.net/10453/120564