Neural network-based compensation control of mobile robots with partially known structure

Autores
Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This study proposes an inverse non-linear controller combined with an adaptive neural network proportional integral (PI) sliding mode using an on-line learning algorithm. The neural network acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations on their dynamics and kinematics. Also, the proposed controller can reduce the steady-state error of a non-linear inverse controller using the on-line adaptive technique based on Lyapunov’s theory. Experimental results 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. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Mobile robots
Neural network
Nonlinear control
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:11336/195461
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Neural network-based compensation control of mobile robots with partially known structureRossomando, Francisco GuidoSoria, Carlos MiguelCarelli Albarracin, Ricardo OscarMobile robotsNeural networkNonlinear controlhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This study proposes an inverse non-linear controller combined with an adaptive neural network proportional integral (PI) sliding mode using an on-line learning algorithm. The neural network acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations on their dynamics and kinematics. Also, the proposed controller can reduce the steady-state error of a non-linear inverse controller using the on-line adaptive technique based on Lyapunov’s theory. Experimental results 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. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaInstitution of Engineering and Technology2012-04info: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/11336/195461Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar; Neural network-based compensation control of mobile robots with partially known structure; Institution of Engineering and Technology; IET Control Theory and Applications; 6; 12; 4-2012; 1851-18601751-8644CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2011.0581info:eu-repo/semantics/altIdentifier/doi/10.1049/iet-cta.2011.0581info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:54:08Zoai:ri.conicet.gov.ar:11336/195461instacron: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:34982025-10-15 14:54:09.177CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Neural network-based compensation control of mobile robots with partially known structure
title Neural network-based compensation control of mobile robots with partially known structure
spellingShingle Neural network-based compensation control of mobile robots with partially known structure
Rossomando, Francisco Guido
Mobile robots
Neural network
Nonlinear control
title_short Neural network-based compensation control of mobile robots with partially known structure
title_full Neural network-based compensation control of mobile robots with partially known structure
title_fullStr Neural network-based compensation control of mobile robots with partially known structure
title_full_unstemmed Neural network-based compensation control of mobile robots with partially known structure
title_sort Neural network-based compensation control of mobile robots with partially known structure
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 Mobile robots
Neural network
Nonlinear control
topic Mobile robots
Neural network
Nonlinear control
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 study proposes an inverse non-linear controller combined with an adaptive neural network proportional integral (PI) sliding mode using an on-line learning algorithm. The neural network acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations on their dynamics and kinematics. Also, the proposed controller can reduce the steady-state error of a non-linear inverse controller using the on-line adaptive technique based on Lyapunov’s theory. Experimental results 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. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description This study proposes an inverse non-linear controller combined with an adaptive neural network proportional integral (PI) sliding mode using an on-line learning algorithm. The neural network acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations on their dynamics and kinematics. Also, the proposed controller can reduce the steady-state error of a non-linear inverse controller using the on-line adaptive technique based on Lyapunov’s theory. Experimental results show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.
publishDate 2012
dc.date.none.fl_str_mv 2012-04
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/11336/195461
Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar; Neural network-based compensation control of mobile robots with partially known structure; Institution of Engineering and Technology; IET Control Theory and Applications; 6; 12; 4-2012; 1851-1860
1751-8644
CONICET Digital
CONICET
url http://hdl.handle.net/11336/195461
identifier_str_mv Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar; Neural network-based compensation control of mobile robots with partially known structure; Institution of Engineering and Technology; IET Control Theory and Applications; 6; 12; 4-2012; 1851-1860
1751-8644
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2011.0581
info:eu-repo/semantics/altIdentifier/doi/10.1049/iet-cta.2011.0581
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 Institution of Engineering and Technology
publisher.none.fl_str_mv Institution of Engineering and Technology
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
_version_ 1846083073054081024
score 13.22299

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