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dc.contributor.authorNapole, Cristian
dc.contributor.authorBarambones Caramazana, Oscar ORCID
dc.contributor.authorDerbeli, Mohamed
dc.contributor.authorCalvo Gordillo, Isidro
dc.contributor.authorSilaa, Mohammed Yousri
dc.contributor.authorVelasco Pascual, Javier
dc.date.accessioned2021-02-09T10:09:45Z
dc.date.available2021-02-09T10:09:45Z
dc.date.issued2021-01-26
dc.identifier.citationMathematics 9(3) : (2021) // Article ID 244es_ES
dc.identifier.issn2227-7390
dc.identifier.urihttp://hdl.handle.net/10810/50118
dc.description.abstractPiezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.es_ES
dc.description.sponsorshipThis research was funded by Basque Government and UPV/EHU projects.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjecthysteresises_ES
dc.subjectcontrol systemses_ES
dc.subjectneural networkses_ES
dc.subjectstabilizationes_ES
dc.subjectactuatorses_ES
dc.subjectsuper twisting algorithmes_ES
dc.titleHigh-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2021-02-05T14:10:55Z
dc.rights.holder2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2227-7390/9/3/244/htmes_ES
dc.identifier.doi10.3390/math9030244
dc.departamentoesIngeniería de sistemas y automática
dc.departamentoeuSistemen ingeniaritza eta automatika


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