dc.contributor.author | Napole, Cristian | |
dc.contributor.author | Barambones Caramazana, Oscar | |
dc.contributor.author | Derbeli, Mohamed | |
dc.contributor.author | Calvo Gordillo, Isidro | |
dc.contributor.author | Silaa, Mohammed Yousri | |
dc.contributor.author | Velasco Pascual, Javier | |
dc.date.accessioned | 2021-02-09T10:09:45Z | |
dc.date.available | 2021-02-09T10:09:45Z | |
dc.date.issued | 2021-01-26 | |
dc.identifier.citation | Mathematics 9(3) : (2021) // Article ID 244 | es_ES |
dc.identifier.issn | 2227-7390 | |
dc.identifier.uri | http://hdl.handle.net/10810/50118 | |
dc.description.abstract | Piezoelectric 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.sponsorship | This research was funded by Basque Government and UPV/EHU projects. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | hysteresis | es_ES |
dc.subject | control systems | es_ES |
dc.subject | neural networks | es_ES |
dc.subject | stabilization | es_ES |
dc.subject | actuators | es_ES |
dc.subject | super twisting algorithm | es_ES |
dc.title | High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2021-02-05T14:10:55Z | |
dc.rights.holder | 2021 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.publisherversion | https://www.mdpi.com/2227-7390/9/3/244/htm | es_ES |
dc.identifier.doi | 10.3390/math9030244 | |
dc.departamentoes | Ingeniería de sistemas y automática | |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | |