Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots

Autores
Montoya Cháirez, Jorge; Moreno Valenzuela, Javier; Santibáñez, Víctor; Carelli Albarracin, Ricardo Oscar; Rossomando, Francisco Guido; Pérez Alcocer, Ricardo
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
By relying on the input–output feedback linearization approach, a novel adaptive controller for flexible joint robots is proposed in this work. First, a model-based controller is developed to get a structure that is useful in the development of the adaptive controller. The adaptive version is developed by using two techniques. To stabilize the output function, an adaptive neural network controller is used, which approximates the non-linear function that contains the uncertainties. The desired rotor position required by the input–output feedback linearization controller is defined with the structure of a link dynamics adaptive regressor-based controller. The main reason to adopt the mentioned structure in the definition of the desired rotor link position is to guarantee its differentiability. Real-time experiment comparisons among the model-based controller, a model-based controller with desired compensation, an adaptive controller based on joint torque feedback, and an adaptive neural network-based controller are carried out. Experimental results support the theory reported in this document and the accuracy of the proposed approach.
Fil: Montoya Cháirez, Jorge. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; México
Fil: Moreno Valenzuela, Javier. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; México
Fil: Santibáñez, Víctor. Instituto Tecnologico de la Laguna; México
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
Fil: Rossomando, Francisco Guido. 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: Pérez Alcocer, Ricardo. Consejo Nacional de Ciencia y Tecnología; México. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; México
Materia
Flexible robots
Adaptive neural networks
Trajectory control
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/210810
Acceder
id CONICETDig_bd70d460d32574812dcfe258e2653c2b
oai_identifier_str oai:ri.conicet.gov.ar:11336/210810
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robotsMontoya Cháirez, JorgeMoreno Valenzuela, JavierSantibáñez, VíctorCarelli Albarracin, Ricardo OscarRossomando, Francisco GuidoPérez Alcocer, RicardoFlexible robotsAdaptive neural networksTrajectory controlhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2By relying on the input–output feedback linearization approach, a novel adaptive controller for flexible joint robots is proposed in this work. First, a model-based controller is developed to get a structure that is useful in the development of the adaptive controller. The adaptive version is developed by using two techniques. To stabilize the output function, an adaptive neural network controller is used, which approximates the non-linear function that contains the uncertainties. The desired rotor position required by the input–output feedback linearization controller is defined with the structure of a link dynamics adaptive regressor-based controller. The main reason to adopt the mentioned structure in the definition of the desired rotor link position is to guarantee its differentiability. Real-time experiment comparisons among the model-based controller, a model-based controller with desired compensation, an adaptive controller based on joint torque feedback, and an adaptive neural network-based controller are carried out. Experimental results support the theory reported in this document and the accuracy of the proposed approach.Fil: Montoya Cháirez, Jorge. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; MéxicoFil: Moreno Valenzuela, Javier. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; MéxicoFil: Santibáñez, Víctor. Instituto Tecnologico de la Laguna; MéxicoFil: 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; ArgentinaFil: Rossomando, Francisco Guido. 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: Pérez Alcocer, Ricardo. Consejo Nacional de Ciencia y Tecnología; México. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; MéxicoInstitution of Engineering and Technology2022-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/210810Montoya Cháirez, Jorge; Moreno Valenzuela, Javier; Santibáñez, Víctor; Carelli Albarracin, Ricardo Oscar; Rossomando, Francisco Guido; et al.; Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots; Institution of Engineering and Technology; IET Control Theory and Applications; 16; 1; 1-2022; 31-501751-8644CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1049/cth2.12202info:eu-repo/semantics/altIdentifier/url/https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cth2.12202info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:36:06Zoai:ri.conicet.gov.ar:11336/210810instacron: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 09:36:06.452CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots
title Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots
spellingShingle Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots
Montoya Cháirez, Jorge
Flexible robots
Adaptive neural networks
Trajectory control
title_short Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots
title_full Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots
title_fullStr Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots
title_full_unstemmed Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots
title_sort Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots
dc.creator.none.fl_str_mv Montoya Cháirez, Jorge
Moreno Valenzuela, Javier
Santibáñez, Víctor
Carelli Albarracin, Ricardo Oscar
Rossomando, Francisco Guido
Pérez Alcocer, Ricardo
author Montoya Cháirez, Jorge
author_facet Montoya Cháirez, Jorge
Moreno Valenzuela, Javier
Santibáñez, Víctor
Carelli Albarracin, Ricardo Oscar
Rossomando, Francisco Guido
Pérez Alcocer, Ricardo
author_role author
author2 Moreno Valenzuela, Javier
Santibáñez, Víctor
Carelli Albarracin, Ricardo Oscar
Rossomando, Francisco Guido
Pérez Alcocer, Ricardo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Flexible robots
Adaptive neural networks
Trajectory control
topic Flexible robots
Adaptive neural networks
Trajectory 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 By relying on the input–output feedback linearization approach, a novel adaptive controller for flexible joint robots is proposed in this work. First, a model-based controller is developed to get a structure that is useful in the development of the adaptive controller. The adaptive version is developed by using two techniques. To stabilize the output function, an adaptive neural network controller is used, which approximates the non-linear function that contains the uncertainties. The desired rotor position required by the input–output feedback linearization controller is defined with the structure of a link dynamics adaptive regressor-based controller. The main reason to adopt the mentioned structure in the definition of the desired rotor link position is to guarantee its differentiability. Real-time experiment comparisons among the model-based controller, a model-based controller with desired compensation, an adaptive controller based on joint torque feedback, and an adaptive neural network-based controller are carried out. Experimental results support the theory reported in this document and the accuracy of the proposed approach.
Fil: Montoya Cháirez, Jorge. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; México
Fil: Moreno Valenzuela, Javier. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; México
Fil: Santibáñez, Víctor. Instituto Tecnologico de la Laguna; México
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
Fil: Rossomando, Francisco Guido. 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: Pérez Alcocer, Ricardo. Consejo Nacional de Ciencia y Tecnología; México. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; México
description By relying on the input–output feedback linearization approach, a novel adaptive controller for flexible joint robots is proposed in this work. First, a model-based controller is developed to get a structure that is useful in the development of the adaptive controller. The adaptive version is developed by using two techniques. To stabilize the output function, an adaptive neural network controller is used, which approximates the non-linear function that contains the uncertainties. The desired rotor position required by the input–output feedback linearization controller is defined with the structure of a link dynamics adaptive regressor-based controller. The main reason to adopt the mentioned structure in the definition of the desired rotor link position is to guarantee its differentiability. Real-time experiment comparisons among the model-based controller, a model-based controller with desired compensation, an adaptive controller based on joint torque feedback, and an adaptive neural network-based controller are carried out. Experimental results support the theory reported in this document and the accuracy of the proposed approach.
publishDate 2022
dc.date.none.fl_str_mv 2022-01
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/11336/210810
Montoya Cháirez, Jorge; Moreno Valenzuela, Javier; Santibáñez, Víctor; Carelli Albarracin, Ricardo Oscar; Rossomando, Francisco Guido; et al.; Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots; Institution of Engineering and Technology; IET Control Theory and Applications; 16; 1; 1-2022; 31-50
1751-8644
CONICET Digital
CONICET
url http://hdl.handle.net/11336/210810
identifier_str_mv Montoya Cháirez, Jorge; Moreno Valenzuela, Javier; Santibáñez, Víctor; Carelli Albarracin, Ricardo Oscar; Rossomando, Francisco Guido; et al.; Combined adaptive neural network and regressor-based trajectory tracking control of flexible joint robots; Institution of Engineering and Technology; IET Control Theory and Applications; 16; 1; 1-2022; 31-50
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/doi/10.1049/cth2.12202
info:eu-repo/semantics/altIdentifier/url/https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cth2.12202
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv 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_ 1844613129969860608
score 13.070432

Publicaciones similares