Autonomous mobile robots navigation using RBF neural compensator

Autores
Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a RBF-NN adaptive dynamic compensation. For a robot with uncertain dynamic parameters,two controllers are implemented separately: a kinematics controller and an inverse dynamics controller. The uncertainty in the nominal dynamics model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The analysis of the RBF-NN approximation error on the control errors is included. Finally,the performance of the control system is verified through experiments.
Fil: Rossomando, Francisco Guido. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Soria, Carlos Miguel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; 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
Adaptive inverse control
MIMO system
Mobile robot 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:11336/246933
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Autonomous mobile robots navigation using RBF neural compensatorRossomando, Francisco GuidoSoria, Carlos MiguelCarelli Albarracin, Ricardo OscarAdaptive inverse controlMIMO systemMobile robot controlhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a RBF-NN adaptive dynamic compensation. For a robot with uncertain dynamic parameters,two controllers are implemented separately: a kinematics controller and an inverse dynamics controller. The uncertainty in the nominal dynamics model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The analysis of the RBF-NN approximation error on the control errors is included. Finally,the performance of the control system is verified through experiments.Fil: Rossomando, Francisco Guido. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Soria, Carlos Miguel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaPergamon-Elsevier Science Ltd2011-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/246933Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar; Autonomous mobile robots navigation using RBF neural compensator; Pergamon-Elsevier Science Ltd; Control Engineering Practice; 19; 3; 3-2011; 215-2220967-0661CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0967066110002583info:eu-repo/semantics/altIdentifier/doi/10.1016/j.conengprac.2010.11.011info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:38:56Zoai:ri.conicet.gov.ar:11336/246933instacron: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 09:38:57.237CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Autonomous mobile robots navigation using RBF neural compensator
title Autonomous mobile robots navigation using RBF neural compensator
spellingShingle Autonomous mobile robots navigation using RBF neural compensator
Rossomando, Francisco Guido
Adaptive inverse control
MIMO system
Mobile robot control
title_short Autonomous mobile robots navigation using RBF neural compensator
title_full Autonomous mobile robots navigation using RBF neural compensator
title_fullStr Autonomous mobile robots navigation using RBF neural compensator
title_full_unstemmed Autonomous mobile robots navigation using RBF neural compensator
title_sort Autonomous mobile robots navigation using RBF neural compensator
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 Adaptive inverse control
MIMO system
Mobile robot control
topic Adaptive inverse control
MIMO system
Mobile robot 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 paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a RBF-NN adaptive dynamic compensation. For a robot with uncertain dynamic parameters,two controllers are implemented separately: a kinematics controller and an inverse dynamics controller. The uncertainty in the nominal dynamics model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The analysis of the RBF-NN approximation error on the control errors is included. Finally,the performance of the control system is verified through experiments.
Fil: Rossomando, Francisco Guido. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Soria, Carlos Miguel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; 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 paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a RBF-NN adaptive dynamic compensation. For a robot with uncertain dynamic parameters,two controllers are implemented separately: a kinematics controller and an inverse dynamics controller. The uncertainty in the nominal dynamics model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The analysis of the RBF-NN approximation error on the control errors is included. Finally,the performance of the control system is verified through experiments.
publishDate 2011
dc.date.none.fl_str_mv 2011-03
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/11336/246933
Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar; Autonomous mobile robots navigation using RBF neural compensator; Pergamon-Elsevier Science Ltd; Control Engineering Practice; 19; 3; 3-2011; 215-222
0967-0661
CONICET Digital
CONICET
url http://hdl.handle.net/11336/246933
identifier_str_mv Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar; Autonomous mobile robots navigation using RBF neural compensator; Pergamon-Elsevier Science Ltd; Control Engineering Practice; 19; 3; 3-2011; 215-222
0967-0661
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://www.sciencedirect.com/science/article/pii/S0967066110002583
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.conengprac.2010.11.011
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
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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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