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dc.contributor.author
Rossomando, Francisco Guido
dc.contributor.author
Soria, Carlos Miguel
dc.contributor.author
Patiño, Daniel
dc.contributor.author
Carelli Albarracin, Ricardo Oscar
dc.date.available
2023-04-03T11:33:34Z
dc.date.issued
2011-04
dc.identifier.citation
Rossomando, Francisco Guido; Soria, Carlos Miguel; Patiño, Daniel; Carelli Albarracin, Ricardo Oscar; Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks; Planta Piloto de Ingeniería Química; Latin American Applied Research; 41; 2; 4-2011; 177-182
dc.identifier.issn
0327-0793
dc.identifier.uri
http://hdl.handle.net/11336/192410
dc.description.abstract
This paper propose an Model Reference Adaptive Control (MRAC) for mobile robots for which stability conditions and performance evaluation are given. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a direct neural network-based adaptive dynamics control. The architecture of the dynamic control is based on radial basis functions neural networks (RBF-NN) to construct the MRAC controller. The parameters of the adaptive dynamic controller are adjusted according to a law derived using Lyapunov stability theory and the centers of the RBF are adapted using the supervised algorithm. The resulting MRAC controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. Stability result for the adaptive neuro-control system is given. It is proved that control errors are ultimately bounded as a function of the approximation error of the RBF-NN. Experimental results showing the practical feasibility and performance of the proposed approach to mobile robotics are given.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Planta Piloto de Ingeniería Química
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.subject
Systems identification
dc.subject
Adaptive neural nets
dc.subject
Mobile robot control
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
Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function 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
2023-03-30T14:45:16Z
dc.identifier.eissn
1851-8796
dc.journal.volume
41
dc.journal.number
2
dc.journal.pagination
177-182
dc.journal.pais
Argentina
dc.journal.ciudad
Bahia Blanca
dc.description.fil
Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
dc.description.fil
Fil: Soria, Carlos Miguel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Patiño, Daniel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
dc.description.fil
Fil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Latin American Applied Research
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S0327-07932011000200012
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