Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique

Autores
Rossomando, Francisco Guido; Serrano, Mario Emanuel; Soria, Carlos Miguel; Scaglia, Gustavo Juan Eduardo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
(is work presents a novel controller for the dynamics of robots using a dynamic variations observer. (e proposed controller uses a saturated control law based on sin(tg− 1(.)) function instead of tanh(.). Besides, this function is an alternative to the use of tanh(.) in saturation control, since it reaches its maximum value more gradually than the hyperbolic tangent function. Using this characteristic, the transition between states is smoother, with similar accuracy to tanh(.). (e controller is designed using a saturated SMC (sliding mode controller) and a dynamic variations observer based on GRNN (general regression neural network). (e originality of this work is the use of a combination of adaptive GRNN with a sliding mode controller (SMC) including a new saturation function. Finally, experiments based on trajectory tracking demonstrate the robustness and simplicity of this method.
Fil: Rossomando, Francisco Guido. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Serrano, Mario Emanuel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; 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. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Materia
Robot manipulator
Saturated Control Technique
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:11336/142784
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control TechniqueRossomando, Francisco GuidoSerrano, Mario EmanuelSoria, Carlos MiguelScaglia, Gustavo Juan EduardoRobot manipulatorSaturated Control TechniqueNonlinear controlhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2(is work presents a novel controller for the dynamics of robots using a dynamic variations observer. (e proposed controller uses a saturated control law based on sin(tg− 1(.)) function instead of tanh(.). Besides, this function is an alternative to the use of tanh(.) in saturation control, since it reaches its maximum value more gradually than the hyperbolic tangent function. Using this characteristic, the transition between states is smoother, with similar accuracy to tanh(.). (e controller is designed using a saturated SMC (sliding mode controller) and a dynamic variations observer based on GRNN (general regression neural network). (e originality of this work is the use of a combination of adaptive GRNN with a sliding mode controller (SMC) including a new saturation function. Finally, experiments based on trajectory tracking demonstrate the robustness and simplicity of this method.Fil: Rossomando, Francisco Guido. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Serrano, Mario Emanuel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; 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. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaHindawi Publishing Corporation2020-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/142784Rossomando, Francisco Guido; Serrano, Mario Emanuel; Soria, Carlos Miguel; Scaglia, Gustavo Juan Eduardo; Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique; Hindawi Publishing Corporation; Mathematical Problems in Engineering; 2020; 5-2020; 1-141024-123X1563-5147CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/mpe/2020/3240210/info:eu-repo/semantics/altIdentifier/doi/10.1155/2020/3240210info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:46:43Zoai:ri.conicet.gov.ar:11336/142784instacron: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 10:46:43.762CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique
title Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique
spellingShingle Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique
Rossomando, Francisco Guido
Robot manipulator
Saturated Control Technique
Nonlinear control
title_short Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique
title_full Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique
title_fullStr Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique
title_full_unstemmed Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique
title_sort Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique
dc.creator.none.fl_str_mv Rossomando, Francisco Guido
Serrano, Mario Emanuel
Soria, Carlos Miguel
Scaglia, Gustavo Juan Eduardo
author Rossomando, Francisco Guido
author_facet Rossomando, Francisco Guido
Serrano, Mario Emanuel
Soria, Carlos Miguel
Scaglia, Gustavo Juan Eduardo
author_role author
author2 Serrano, Mario Emanuel
Soria, Carlos Miguel
Scaglia, Gustavo Juan Eduardo
author2_role author
author
author
dc.subject.none.fl_str_mv Robot manipulator
Saturated Control Technique
Nonlinear control
topic Robot manipulator
Saturated Control Technique
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 (is work presents a novel controller for the dynamics of robots using a dynamic variations observer. (e proposed controller uses a saturated control law based on sin(tg− 1(.)) function instead of tanh(.). Besides, this function is an alternative to the use of tanh(.) in saturation control, since it reaches its maximum value more gradually than the hyperbolic tangent function. Using this characteristic, the transition between states is smoother, with similar accuracy to tanh(.). (e controller is designed using a saturated SMC (sliding mode controller) and a dynamic variations observer based on GRNN (general regression neural network). (e originality of this work is the use of a combination of adaptive GRNN with a sliding mode controller (SMC) including a new saturation function. Finally, experiments based on trajectory tracking demonstrate the robustness and simplicity of this method.
Fil: Rossomando, Francisco Guido. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Serrano, Mario Emanuel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; 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. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
description (is work presents a novel controller for the dynamics of robots using a dynamic variations observer. (e proposed controller uses a saturated control law based on sin(tg− 1(.)) function instead of tanh(.). Besides, this function is an alternative to the use of tanh(.) in saturation control, since it reaches its maximum value more gradually than the hyperbolic tangent function. Using this characteristic, the transition between states is smoother, with similar accuracy to tanh(.). (e controller is designed using a saturated SMC (sliding mode controller) and a dynamic variations observer based on GRNN (general regression neural network). (e originality of this work is the use of a combination of adaptive GRNN with a sliding mode controller (SMC) including a new saturation function. Finally, experiments based on trajectory tracking demonstrate the robustness and simplicity of this method.
publishDate 2020
dc.date.none.fl_str_mv 2020-05
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/11336/142784
Rossomando, Francisco Guido; Serrano, Mario Emanuel; Soria, Carlos Miguel; Scaglia, Gustavo Juan Eduardo; Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique; Hindawi Publishing Corporation; Mathematical Problems in Engineering; 2020; 5-2020; 1-14
1024-123X
1563-5147
CONICET Digital
CONICET
url http://hdl.handle.net/11336/142784
identifier_str_mv Rossomando, Francisco Guido; Serrano, Mario Emanuel; Soria, Carlos Miguel; Scaglia, Gustavo Juan Eduardo; Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique; Hindawi Publishing Corporation; Mathematical Problems in Engineering; 2020; 5-2020; 1-14
1024-123X
1563-5147
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.hindawi.com/journals/mpe/2020/3240210/
info:eu-repo/semantics/altIdentifier/doi/10.1155/2020/3240210
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
dc.publisher.none.fl_str_mv Hindawi Publishing Corporation
publisher.none.fl_str_mv Hindawi Publishing Corporation
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_ 1844614509448134656
score 13.070432

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