Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks

Autores
Lopez Sanchez, Ivan; Rossomando, Francisco Guido; Pérez Alcocer, Ricardo; Soria, Carlos Miguel; Carelli, Ricardo; Moreno Valenzuela, Javier
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.
Fil: Lopez Sanchez, Ivan. INSTITUTO POLITÉCNICO NACIONAL (IPN);
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. INSTITUTO POLITÉCNICO NACIONAL (IPN);
Fil: Soria, Carlos Miguel. 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: Carelli, Ricardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Moreno Valenzuela, Javier. INSTITUTO POLITÉCNICO NACIONAL (IPN);
Materia
ADAPTIVE CONTROL
GENERALIZED REGRESSION NEURAL NETWORK
QUADROTOR
REAL-TIME EXPERIMENTS
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:11336/183239
Acceder
id CONICETDig_b64070db9f49c5e65224cfd3eda239ae
oai_identifier_str oai:ri.conicet.gov.ar:11336/183239
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networksLopez Sanchez, IvanRossomando, Francisco GuidoPérez Alcocer, RicardoSoria, Carlos MiguelCarelli, RicardoMoreno Valenzuela, JavierADAPTIVE CONTROLGENERALIZED REGRESSION NEURAL NETWORKQUADROTORREAL-TIME EXPERIMENTShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.Fil: Lopez Sanchez, Ivan. INSTITUTO POLITÉCNICO NACIONAL (IPN);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; ArgentinaFil: Pérez Alcocer, Ricardo. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Soria, Carlos Miguel. 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: Carelli, Ricardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Moreno Valenzuela, Javier. INSTITUTO POLITÉCNICO NACIONAL (IPN);Elsevier Science2021-06info: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/11336/183239Lopez Sanchez, Ivan; Rossomando, Francisco Guido; Pérez Alcocer, Ricardo; Soria, Carlos Miguel; Carelli, Ricardo; et al.; Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks; Elsevier Science; Neurocomputing; 460; 6-2021; 243-2550925-2312CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0925231221010092info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neucom.2021.06.079info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:36:05Zoai:ri.conicet.gov.ar:11336/183239instacron: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 09:36:05.295CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
title Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
spellingShingle Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
Lopez Sanchez, Ivan
ADAPTIVE CONTROL
GENERALIZED REGRESSION NEURAL NETWORK
QUADROTOR
REAL-TIME EXPERIMENTS
title_short Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
title_full Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
title_fullStr Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
title_full_unstemmed Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
title_sort Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
dc.creator.none.fl_str_mv Lopez Sanchez, Ivan
Rossomando, Francisco Guido
Pérez Alcocer, Ricardo
Soria, Carlos Miguel
Carelli, Ricardo
Moreno Valenzuela, Javier
author Lopez Sanchez, Ivan
author_facet Lopez Sanchez, Ivan
Rossomando, Francisco Guido
Pérez Alcocer, Ricardo
Soria, Carlos Miguel
Carelli, Ricardo
Moreno Valenzuela, Javier
author_role author
author2 Rossomando, Francisco Guido
Pérez Alcocer, Ricardo
Soria, Carlos Miguel
Carelli, Ricardo
Moreno Valenzuela, Javier
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv ADAPTIVE CONTROL
GENERALIZED REGRESSION NEURAL NETWORK
QUADROTOR
REAL-TIME EXPERIMENTS
topic ADAPTIVE CONTROL
GENERALIZED REGRESSION NEURAL NETWORK
QUADROTOR
REAL-TIME EXPERIMENTS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.
Fil: Lopez Sanchez, Ivan. INSTITUTO POLITÉCNICO NACIONAL (IPN);
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. INSTITUTO POLITÉCNICO NACIONAL (IPN);
Fil: Soria, Carlos Miguel. 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: Carelli, Ricardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Moreno Valenzuela, Javier. INSTITUTO POLITÉCNICO NACIONAL (IPN);
description In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.
publishDate 2021
dc.date.none.fl_str_mv 2021-06
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/11336/183239
Lopez Sanchez, Ivan; Rossomando, Francisco Guido; Pérez Alcocer, Ricardo; Soria, Carlos Miguel; Carelli, Ricardo; et al.; Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks; Elsevier Science; Neurocomputing; 460; 6-2021; 243-255
0925-2312
CONICET Digital
CONICET
url http://hdl.handle.net/11336/183239
identifier_str_mv Lopez Sanchez, Ivan; Rossomando, Francisco Guido; Pérez Alcocer, Ricardo; Soria, Carlos Miguel; Carelli, Ricardo; et al.; Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks; Elsevier Science; Neurocomputing; 460; 6-2021; 243-255
0925-2312
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://linkinghub.elsevier.com/retrieve/pii/S0925231221010092
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neucom.2021.06.079
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
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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_ 1844613129102688256
score 13.070432

Publicaciones similares