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Authordc.contributor.authorGallegos, Javier A. 
Authordc.contributor.authorDuarte Mermoud, Manuel 
Admission datedc.date.accessioned2018-05-22T14:58:32Z
Available datedc.date.available2018-05-22T14:58:32Z
Publication datedc.date.issued2017
Cita de ítemdc.identifier.citationISA Transactions 69 (2017): 31–42es_ES
Identifierdc.identifier.other10.1016/j.isatra.2017.04.021
Identifierdc.identifier.urihttps://repositorio.uchile.cl/handle/2250/147991
Abstractdc.description.abstractConditions for boundedness and convergence of the output error and the parameter error for various Caputo's fractional order adaptive schemes based on the steepest descent method are derived in this paper. To this aim, the concept of sufficiently exciting signals is introduced, characterized and related to the concept of persistently exciting signals used in the integer order case. An application is designed in adaptive indirect control of integer order systems using fractional equations to adjust parameters. This application is illustrated for a pole placement adaptive problem. Advantages of using fractional adjustment in control adaptive schemes are experimentally obtained. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.es_ES
Patrocinadordc.description.sponsorshipCONICYT-Chile, FB0809 / FONDECYT, 1150488es_ES
Lenguagedc.language.isoenes_ES
Publisherdc.publisherElsevieres_ES
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile*
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
Sourcedc.sourceISA Transactionses_ES
Keywordsdc.subjectAdaptive systemses_ES
Keywordsdc.subjectFractional systemses_ES
Keywordsdc.subjectGradient methodes_ES
Títulodc.titleConvergence of fractional adaptive systems using gradient approaches_ES
Document typedc.typeArtículo de revista
Catalogueruchile.catalogadortjnes_ES
Indexationuchile.indexArtículo de publicación ISIes_ES


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Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile