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dc.contributor.author
Lopez Sanchez, Ivan
dc.contributor.author
Rossomando, Francisco Guido
dc.contributor.author
Pérez Alcocer, Ricardo
dc.contributor.author
Soria, Carlos Miguel
dc.contributor.author
Carelli, Ricardo
dc.contributor.author
Moreno Valenzuela, Javier
dc.date.available
2023-01-04T11:10:25Z
dc.date.issued
2021-06
dc.identifier.citation
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
dc.identifier.issn
0925-2312
dc.identifier.uri
http://hdl.handle.net/11336/183239
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
ADAPTIVE CONTROL
dc.subject
GENERALIZED REGRESSION NEURAL NETWORK
dc.subject
QUADROTOR
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REAL-TIME EXPERIMENTS
dc.subject.classification
Control Automático y Robótica
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-09-21T11:55:05Z
dc.journal.volume
460
dc.journal.pagination
243-255
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Lopez Sanchez, Ivan. INSTITUTO POLITÉCNICO NACIONAL (IPN);
dc.description.fil
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
dc.description.fil
Fil: Pérez Alcocer, Ricardo. INSTITUTO POLITÉCNICO NACIONAL (IPN);
dc.description.fil
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
dc.description.fil
Fil: Carelli, Ricardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
dc.description.fil
Fil: Moreno Valenzuela, Javier. INSTITUTO POLITÉCNICO NACIONAL (IPN);
dc.journal.title
Neurocomputing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0925231221010092
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.neucom.2021.06.079
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