Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks

Autores
Rossomando, Francisco Guido; Soria, Carlos Miguel; Patiño, Daniel; Carelli Albarracin, Ricardo Oscar
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper propose an Model Reference Adaptive Control (MRAC) for mobile robots for which stability conditions and performance evaluation are given. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a direct neural network-based adaptive dynamics control. The architecture of the dynamic control is based on radial basis functions neural networks (RBF-NN) to construct the MRAC controller. The parameters of the adaptive dynamic controller are adjusted according to a law derived using Lyapunov stability theory and the centers of the RBF are adapted using the supervised algorithm. The resulting MRAC controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. Stability result for the adaptive neuro-control system is given. It is proved that control errors are ultimately bounded as a function of the approximation error of the RBF-NN. Experimental results showing the practical feasibility and performance of the proposed approach to mobile robotics are given.
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. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Patiño, Daniel. 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
Systems identification
Adaptive neural nets
Mobile robot control
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/192410
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/192410
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural NetworksRossomando, Francisco GuidoSoria, Carlos MiguelPatiño, DanielCarelli Albarracin, Ricardo OscarSystems identificationAdaptive neural netsMobile robot controlhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper propose an Model Reference Adaptive Control (MRAC) for mobile robots for which stability conditions and performance evaluation are given. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a direct neural network-based adaptive dynamics control. The architecture of the dynamic control is based on radial basis functions neural networks (RBF-NN) to construct the MRAC controller. The parameters of the adaptive dynamic controller are adjusted according to a law derived using Lyapunov stability theory and the centers of the RBF are adapted using the supervised algorithm. The resulting MRAC controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. Stability result for the adaptive neuro-control system is given. It is proved that control errors are ultimately bounded as a function of the approximation error of the RBF-NN. Experimental results showing the practical feasibility and performance of the proposed approach to mobile robotics are given.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. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Patiño, Daniel. 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; ArgentinaPlanta Piloto de Ingeniería Química2011-04info: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/11336/192410Rossomando, Francisco Guido; Soria, Carlos Miguel; Patiño, Daniel; Carelli Albarracin, Ricardo Oscar; Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks; Planta Piloto de Ingeniería Química; Latin American Applied Research; 41; 2; 4-2011; 177-1820327-07931851-8796CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S0327-07932011000200012info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:21:50Zoai:ri.conicet.gov.ar:11336/192410instacron: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 10:21:50.751CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks
title Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks
spellingShingle Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks
Rossomando, Francisco Guido
Systems identification
Adaptive neural nets
Mobile robot control
title_short Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks
title_full Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks
title_fullStr Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks
title_full_unstemmed Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks
title_sort Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks
dc.creator.none.fl_str_mv Rossomando, Francisco Guido
Soria, Carlos Miguel
Patiño, Daniel
Carelli Albarracin, Ricardo Oscar
author Rossomando, Francisco Guido
author_facet Rossomando, Francisco Guido
Soria, Carlos Miguel
Patiño, Daniel
Carelli Albarracin, Ricardo Oscar
author_role author
author2 Soria, Carlos Miguel
Patiño, Daniel
Carelli Albarracin, Ricardo Oscar
author2_role author
author
author
dc.subject.none.fl_str_mv Systems identification
Adaptive neural nets
Mobile robot control
topic Systems identification
Adaptive neural nets
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 propose an Model Reference Adaptive Control (MRAC) for mobile robots for which stability conditions and performance evaluation are given. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a direct neural network-based adaptive dynamics control. The architecture of the dynamic control is based on radial basis functions neural networks (RBF-NN) to construct the MRAC controller. The parameters of the adaptive dynamic controller are adjusted according to a law derived using Lyapunov stability theory and the centers of the RBF are adapted using the supervised algorithm. The resulting MRAC controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. Stability result for the adaptive neuro-control system is given. It is proved that control errors are ultimately bounded as a function of the approximation error of the RBF-NN. Experimental results showing the practical feasibility and performance of the proposed approach to mobile robotics are given.
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. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Patiño, Daniel. 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 paper propose an Model Reference Adaptive Control (MRAC) for mobile robots for which stability conditions and performance evaluation are given. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a direct neural network-based adaptive dynamics control. The architecture of the dynamic control is based on radial basis functions neural networks (RBF-NN) to construct the MRAC controller. The parameters of the adaptive dynamic controller are adjusted according to a law derived using Lyapunov stability theory and the centers of the RBF are adapted using the supervised algorithm. The resulting MRAC controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. Stability result for the adaptive neuro-control system is given. It is proved that control errors are ultimately bounded as a function of the approximation error of the RBF-NN. Experimental results showing the practical feasibility and performance of the proposed approach to mobile robotics are given.
publishDate 2011
dc.date.none.fl_str_mv 2011-04
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/11336/192410
Rossomando, Francisco Guido; Soria, Carlos Miguel; Patiño, Daniel; Carelli Albarracin, Ricardo Oscar; Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks; Planta Piloto de Ingeniería Química; Latin American Applied Research; 41; 2; 4-2011; 177-182
0327-0793
1851-8796
CONICET Digital
CONICET
url http://hdl.handle.net/11336/192410
identifier_str_mv Rossomando, Francisco Guido; Soria, Carlos Miguel; Patiño, Daniel; Carelli Albarracin, Ricardo Oscar; Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks; Planta Piloto de Ingeniería Química; Latin American Applied Research; 41; 2; 4-2011; 177-182
0327-0793
1851-8796
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.scielo.org.ar/scielo.php?script=sci_arttext&pid=S0327-07932011000200012
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Planta Piloto de Ingeniería Química
publisher.none.fl_str_mv Planta Piloto de Ingeniería Química
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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