Mostrar el registro sencillo del ítem
dc.contributor.author
Gandolfo, Daniel
dc.contributor.author
Rossomando, Francisco Guido
dc.contributor.author
Soria, Carlos Miguel
dc.contributor.author
Carelli Albarracin, Ricardo Oscar
dc.date.available
2021-02-04T20:39:34Z
dc.date.issued
2019-04
dc.identifier.citation
Gandolfo, Daniel; Rossomando, Francisco Guido; Soria, Carlos Miguel; Carelli Albarracin, Ricardo Oscar; Adaptive Neural Compensator for Robotic Systems Control; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 17; 4; 4-2019; 670-676
dc.identifier.issn
1548-0992
dc.identifier.uri
http://hdl.handle.net/11336/124880
dc.description.abstract
In the area of robotics systems, there are numerous applications where robots are expected to move rapidly from one place to another, or follow desired trajectories while maintaining good dynamic behavior. However, certain non-linearities, uncertainties in dynamics and external perturbations make the design of ideal controllers a complicated task in many situations. In this paper, we propose a control scheme that combines a nominal feedback controller with a classical PD and a robust adaptive compensator based on artificial neural networks. Using this control scheme, it is possible to obtain a fully tuned compensation parameters and a strong robustness with respect to uncertain dynamics and different non-linearities, as well as to keep the output tracking error bounded to values close to zero. In order to show the performance of the proposed technique, a SCARA (Selective Compliant Articulated Robot Arm) type robot with two degrees of freedom is considered in this case; but this control proposal can be applied to different systems with dynamic variations. The efficiency and performance of the control law is demonstrated through simulation results and the stability analysis is carried out using Lyapunov's theory.
dc.format
application/pdf
dc.language.iso
spa
dc.publisher
Institute of Electrical and Electronics Engineers
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ADAPTIVE CONTROL
dc.subject
ARTIFICIAL NEURAL NETWORK
dc.subject
IDENTIFICATION
dc.subject
ROBOT MANIPULATOR
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 Neural Compensator for Robotic Systems Control
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-11-19T21:46:08Z
dc.journal.volume
17
dc.journal.number
4
dc.journal.pagination
670-676
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Gandolfo, Daniel. 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: 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: 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 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
dc.journal.title
IEEE Latin America Transactions
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/abstract/document/8891932
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TLA.2019.8891932
Archivos asociados