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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorLoidi Eguren, Ion
dc.contributor.otherGómez Espinosa, A.
dc.contributor.otherCastro Sundin, Roberto
dc.contributor.otherCuan Urquizo, Enrique
dc.contributor.otherTreviño Quintanilla, Cecilia D.
dc.date.accessioned2020-06-12T07:50:14Z
dc.date.available2020-06-12T07:50:14Z
dc.date.issued2019
dc.identifier.issn1424-8220en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=155426en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/1685
dc.description.abstractNew actuators and materials are constantly incorporated into industrial processes, and additional challenges are posed by their complex behavior. Nonlinear hysteresis is commonly found in shape memory alloys, and the inclusion of a suitable hysteresis model in the control system allows the controller to achieve a better performance, although a major drawback is that each system responds in a unique way. In this work, a neural network direct control, with online learning, is developed for position control of shape memory alloy manipulators. Neural network weight coefficients are updated online by using the actuator position data while the controller is applied to the system, without previous training of the neural network weights, nor the inclusion of a hysteresis model. A real-time, low computational cost control system was implemented; experimental evaluation was performed on a 1-DOF manipulator system actuated by a shape memory alloy wire. Test results verified the effectiveness of the proposed control scheme to control the system angular position, compensating for the hysteretic behavior of the shape memory alloy actuator. Using a learning algorithm with a sine wave as reference signal, a maximum static error of 0.83º was achieved when validated against several set-points within the possible range.en
dc.language.isoengen
dc.publisherMDPI AGen
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerlanden
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectshape memory alloysen
dc.subjectartificial neural networken
dc.subjectcontrolen
dc.subjectmanipulatorsen
dc.titleNeural Network Direct Control with Online Learning for Shape Memory Alloy Manipulatorsen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.sourceSensorsen
dc.description.versioninfo:eu-repo/semantics/publishedVersionen
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/s19112576en
local.rights.publicationfeeAPCen
local.rights.publicationfeeamount1870 EURen
local.contributor.otherinstitutionTecnológico de Monterreyes
local.contributor.otherinstitutionKTH Royal Institute of Technologyes
local.source.detailsVol.19. N. 11. N. artículo 2576, 2019eu_ES


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International