A data dropout compensation algorithm based on the iterative learning control methodology for discrete-time systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input...

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Autores: Alonso-Quesada, Santiago|||0000-0002-4724-7583, De la Sen, Manuel|||0000-0001-9320-9433, Ibeas, Asier|||0000-0001-5094-3152
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:215261
Acceso en línea:https://ddd.uab.cat/record/215261
https://dx.doi.org/urn:doi:10.1155/2015/429892
Access Level:acceso abierto
Palabra clave:Compensation algorithm
Discrete - time systems
Iterative learning control
Iterative learning control systems
Iterative learning laws
Measurements of
Sampling instants
Through transmission
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spelling A data dropout compensation algorithm based on the iterative learning control methodology for discrete-time systemsAlonso-Quesada, Santiago|||0000-0002-4724-7583De la Sen, Manuel|||0000-0001-9320-9433Ibeas, Asier|||0000-0001-5094-3152Compensation algorithmDiscrete - time systemsIterative learning controlIterative learning control systemsIterative learning lawsMeasurements ofSampling instantsThrough transmissionThis paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity. 22015-01-0120152015-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/215261https://dx.doi.org/urn:doi:10.1155/2015/429892reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengMinisterio de Economía y Competitividad https://doi.org/10.13039/501100003329 DPI2012-30651open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2152612026-06-06T12:50:31Z
dc.title.none.fl_str_mv A data dropout compensation algorithm based on the iterative learning control methodology for discrete-time systems
title A data dropout compensation algorithm based on the iterative learning control methodology for discrete-time systems
spellingShingle A data dropout compensation algorithm based on the iterative learning control methodology for discrete-time systems
Alonso-Quesada, Santiago|||0000-0002-4724-7583
Compensation algorithm
Discrete - time systems
Iterative learning control
Iterative learning control systems
Iterative learning laws
Measurements of
Sampling instants
Through transmission
title_short A data dropout compensation algorithm based on the iterative learning control methodology for discrete-time systems
title_full A data dropout compensation algorithm based on the iterative learning control methodology for discrete-time systems
title_fullStr A data dropout compensation algorithm based on the iterative learning control methodology for discrete-time systems
title_full_unstemmed A data dropout compensation algorithm based on the iterative learning control methodology for discrete-time systems
title_sort A data dropout compensation algorithm based on the iterative learning control methodology for discrete-time systems
dc.creator.none.fl_str_mv Alonso-Quesada, Santiago|||0000-0002-4724-7583
De la Sen, Manuel|||0000-0001-9320-9433
Ibeas, Asier|||0000-0001-5094-3152
author Alonso-Quesada, Santiago|||0000-0002-4724-7583
author_facet Alonso-Quesada, Santiago|||0000-0002-4724-7583
De la Sen, Manuel|||0000-0001-9320-9433
Ibeas, Asier|||0000-0001-5094-3152
author_role author
author2 De la Sen, Manuel|||0000-0001-9320-9433
Ibeas, Asier|||0000-0001-5094-3152
author2_role author
author
dc.subject.none.fl_str_mv Compensation algorithm
Discrete - time systems
Iterative learning control
Iterative learning control systems
Iterative learning laws
Measurements of
Sampling instants
Through transmission
topic Compensation algorithm
Discrete - time systems
Iterative learning control
Iterative learning control systems
Iterative learning laws
Measurements of
Sampling instants
Through transmission
description This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.
publishDate 2015
dc.date.none.fl_str_mv 2
2015-01-01
2015
2015-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/215261
https://dx.doi.org/urn:doi:10.1155/2015/429892
url https://ddd.uab.cat/record/215261
https://dx.doi.org/urn:doi:10.1155/2015/429892
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad https://doi.org/10.13039/501100003329 DPI2012-30651
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301629

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