Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties

Autores
Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.
Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Provincia de San Juan; Argentina. Gobierno de la Provincia de San Juan. Secretaria de Estado de Ciencia, Tecnología e Innovación. Subsecretaria de Promoción de la Actividad Científica; Argentina
Fil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Materia
Nonlinear Systems
Neural Networks
Mimo Systems
Sliding Mode Control
Radial Basis Functions
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/25357
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertaintiesRossomando, Francisco GuidoSoria, Carlos MiguelCarelli Albarracin, Ricardo OscarNonlinear SystemsNeural NetworksMimo SystemsSliding Mode ControlRadial Basis Functionshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Provincia de San Juan; Argentina. Gobierno de la Provincia de San Juan. Secretaria de Estado de Ciencia, Tecnología e Innovación. Subsecretaria de Promoción de la Actividad Científica; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaElsevier2013-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/25357Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar; Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties; Elsevier; Engineering Applications Of Artificial Intelligence; 26; 10; 9-2013; 2251-22590952-1976CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0952197613001656info:eu-repo/semantics/altIdentifier/doi/10.1016/j.engappai.2013.08.008info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:36:14Zoai:ri.conicet.gov.ar:11336/25357instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:36:14.362CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
title Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
spellingShingle Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
Rossomando, Francisco Guido
Nonlinear Systems
Neural Networks
Mimo Systems
Sliding Mode Control
Radial Basis Functions
title_short Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
title_full Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
title_fullStr Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
title_full_unstemmed Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
title_sort Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
dc.creator.none.fl_str_mv Rossomando, Francisco Guido
Soria, Carlos Miguel
Carelli Albarracin, Ricardo Oscar
author Rossomando, Francisco Guido
author_facet Rossomando, Francisco Guido
Soria, Carlos Miguel
Carelli Albarracin, Ricardo Oscar
author_role author
author2 Soria, Carlos Miguel
Carelli Albarracin, Ricardo Oscar
author2_role author
author
dc.subject.none.fl_str_mv Nonlinear Systems
Neural Networks
Mimo Systems
Sliding Mode Control
Radial Basis Functions
topic Nonlinear Systems
Neural Networks
Mimo Systems
Sliding Mode Control
Radial Basis Functions
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.
Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Provincia de San Juan; Argentina. Gobierno de la Provincia de San Juan. Secretaria de Estado de Ciencia, Tecnología e Innovación. Subsecretaria de Promoción de la Actividad Científica; Argentina
Fil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
description This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.
publishDate 2013
dc.date.none.fl_str_mv 2013-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/25357
Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar; Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties; Elsevier; Engineering Applications Of Artificial Intelligence; 26; 10; 9-2013; 2251-2259
0952-1976
CONICET Digital
CONICET
url http://hdl.handle.net/11336/25357
identifier_str_mv Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar; Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties; Elsevier; Engineering Applications Of Artificial Intelligence; 26; 10; 9-2013; 2251-2259
0952-1976
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0952197613001656
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.engappai.2013.08.008
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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