Comparison of frequency response and neural network techniques for system identification of an actively controlled structure

System identification methodsare generally used to obtain the dynamic properties of structural systems. The dynamic properties are used for various purposes, such as model updating, structural health monitoring, and control synthesis. This paper presents the identification of an actively controlled...

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Autores:
Gómez Pizano, Daniel
Tipo de recurso:
Article of journal
Fecha de publicación:
2011
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/40453
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/40453
http://bdigital.unal.edu.co/30550/
Palabra clave:
Structural dynamics and control
system identification
frequency response
artificial neural networks
MISOsystem
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
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spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gómez Pizano, Daniela04ecf20-ce00-4a56-abfb-a2fac7050f123002019-06-28T09:33:55Z2019-06-28T09:33:55Z2011https://repositorio.unal.edu.co/handle/unal/40453http://bdigital.unal.edu.co/30550/System identification methodsare generally used to obtain the dynamic properties of structural systems. The dynamic properties are used for various purposes, such as model updating, structural health monitoring, and control synthesis. This paper presents the identification of an actively controlled structure with an active mass damper based on input-outputrelationships.The input signals include accelerations in the base of the structure and control force inputs while the output signals are the accelerations of the structure due to the inputs. In this paper, the system identification using frequency response functions iscompared with non-linear relationships obtained by using artificial neural networks (ANN) for bothasingle-input, single-output, and multiple-inputsingle-output (MISO) system. The results indicate that for the MISO structural system,the ANN technique providesa more accurate identification than identifications obtained with frequency responsemethods.application/pdfspaUniversidad Nacional de Colombia Sede Medellínhttp://revistas.unal.edu.co/index.php/dyna/article/view/29390Universidad Nacional de Colombia Revistas electrónicas UN DynaDynaDyna; Vol. 78, núm. 170 (2011); 79-89 DYNA; Vol. 78, núm. 170 (2011); 79-89 2346-2183 0012-7353Gómez Pizano, Daniel (2011) Comparison of frequency response and neural network techniques for system identification of an actively controlled structure. Dyna; Vol. 78, núm. 170 (2011); 79-89 DYNA; Vol. 78, núm. 170 (2011); 79-89 2346-2183 0012-7353 .Comparison of frequency response and neural network techniques for system identification of an actively controlled structureArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTStructural dynamics and controlsystem identificationfrequency responseartificial neural networksMISOsystemORIGINAL29390-105927-1-PB.pdfapplication/pdf1865946https://repositorio.unal.edu.co/bitstream/unal/40453/1/29390-105927-1-PB.pdfae2f205a74c8511ec0090a7297feda84MD51THUMBNAIL29390-105927-1-PB.pdf.jpg29390-105927-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg9105https://repositorio.unal.edu.co/bitstream/unal/40453/2/29390-105927-1-PB.pdf.jpgb18709334e3cb5ad8628330b1462cd88MD52unal/40453oai:repositorio.unal.edu.co:unal/404532024-01-25 23:06:23.246Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv Comparison of frequency response and neural network techniques for system identification of an actively controlled structure
title Comparison of frequency response and neural network techniques for system identification of an actively controlled structure
spellingShingle Comparison of frequency response and neural network techniques for system identification of an actively controlled structure
Structural dynamics and control
system identification
frequency response
artificial neural networks
MISOsystem
title_short Comparison of frequency response and neural network techniques for system identification of an actively controlled structure
title_full Comparison of frequency response and neural network techniques for system identification of an actively controlled structure
title_fullStr Comparison of frequency response and neural network techniques for system identification of an actively controlled structure
title_full_unstemmed Comparison of frequency response and neural network techniques for system identification of an actively controlled structure
title_sort Comparison of frequency response and neural network techniques for system identification of an actively controlled structure
dc.creator.fl_str_mv Gómez Pizano, Daniel
dc.contributor.author.spa.fl_str_mv Gómez Pizano, Daniel
dc.subject.proposal.spa.fl_str_mv Structural dynamics and control
system identification
frequency response
artificial neural networks
MISOsystem
topic Structural dynamics and control
system identification
frequency response
artificial neural networks
MISOsystem
description System identification methodsare generally used to obtain the dynamic properties of structural systems. The dynamic properties are used for various purposes, such as model updating, structural health monitoring, and control synthesis. This paper presents the identification of an actively controlled structure with an active mass damper based on input-outputrelationships.The input signals include accelerations in the base of the structure and control force inputs while the output signals are the accelerations of the structure due to the inputs. In this paper, the system identification using frequency response functions iscompared with non-linear relationships obtained by using artificial neural networks (ANN) for bothasingle-input, single-output, and multiple-inputsingle-output (MISO) system. The results indicate that for the MISO structural system,the ANN technique providesa more accurate identification than identifications obtained with frequency responsemethods.
publishDate 2011
dc.date.issued.spa.fl_str_mv 2011
dc.date.accessioned.spa.fl_str_mv 2019-06-28T09:33:55Z
dc.date.available.spa.fl_str_mv 2019-06-28T09:33:55Z
dc.type.spa.fl_str_mv Artículo de revista
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format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
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url https://repositorio.unal.edu.co/handle/unal/40453
http://bdigital.unal.edu.co/30550/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv http://revistas.unal.edu.co/index.php/dyna/article/view/29390
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Dyna
Dyna
dc.relation.ispartofseries.none.fl_str_mv Dyna; Vol. 78, núm. 170 (2011); 79-89 DYNA; Vol. 78, núm. 170 (2011); 79-89 2346-2183 0012-7353
dc.relation.references.spa.fl_str_mv Gómez Pizano, Daniel (2011) Comparison of frequency response and neural network techniques for system identification of an actively controlled structure. Dyna; Vol. 78, núm. 170 (2011); 79-89 DYNA; Vol. 78, núm. 170 (2011); 79-89 2346-2183 0012-7353 .
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia Sede Medellín
institution Universidad Nacional de Colombia
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