Estimating largest Lyapunov exponents in aeroelastic signals

Detalhes bibliográficos
Autor(a) principal: Nascimento Neto, Jayro do [UNESP]
Data de Publicação: 2022
Outros Autores: Vasconcellos, Rui Marcos Grombone de [UNESP], Ferreira, André Alves [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s40435-021-00833-0
http://hdl.handle.net/11449/240111
Resumo: The characterization of experimental signals from nonlinear systems is of fundamental importance for the development of precise mathematical models. Aeroelastic systems are intrinsically nonlinear, leading to unpredictable and sometimes uncontrollable situations. Experimental signal analysis still represents the most effective way to characterize most of the phenomena that occur in the different operating conditions of aircraft. Nonlinear signal analysis tools allow the identification and characterization of a nonlinear system, where one of the most important characteristics is the classification of the behavior between stability, instability and the type of instability so that prevention or control techniques can be implemented. However, techniques for characterizing nonlinear behavior, such as determining the largest Lyapunov exponent from the reconstructed state space, can be affected by noise, always present in experimental signals, resulting in an incorrect characterization. In this work, the singular value decomposition method is first applied as state space reconstruction and as a digital filter to the well-known Lorenz attractor at different levels of noise contamination. The effects of noise on the estimation of the largest Lyapunov exponent and the efficiency of the proposed signal filtering technique are discussed. With the procedure verified, the method is applied in experimental aeroelastic signals from a wind tunnel test of a three degrees of freedom aeroelastic wing with freeplay nonlinearity in the control surface. Subsequently, the largest Lyapunov exponent is estimated to characterize the nonlinear behavior.
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spelling Estimating largest Lyapunov exponents in aeroelastic signalsAeroelastic signalsChaosLaypunov exponentsNoiseNonlinear systemsSingular value decompositionThe characterization of experimental signals from nonlinear systems is of fundamental importance for the development of precise mathematical models. Aeroelastic systems are intrinsically nonlinear, leading to unpredictable and sometimes uncontrollable situations. Experimental signal analysis still represents the most effective way to characterize most of the phenomena that occur in the different operating conditions of aircraft. Nonlinear signal analysis tools allow the identification and characterization of a nonlinear system, where one of the most important characteristics is the classification of the behavior between stability, instability and the type of instability so that prevention or control techniques can be implemented. However, techniques for characterizing nonlinear behavior, such as determining the largest Lyapunov exponent from the reconstructed state space, can be affected by noise, always present in experimental signals, resulting in an incorrect characterization. In this work, the singular value decomposition method is first applied as state space reconstruction and as a digital filter to the well-known Lorenz attractor at different levels of noise contamination. The effects of noise on the estimation of the largest Lyapunov exponent and the efficiency of the proposed signal filtering technique are discussed. With the procedure verified, the method is applied in experimental aeroelastic signals from a wind tunnel test of a three degrees of freedom aeroelastic wing with freeplay nonlinearity in the control surface. Subsequently, the largest Lyapunov exponent is estimated to characterize the nonlinear behavior.São Paulo State University (UNESP), Av. Profa. Isette Corrêa Fontão, 505, SPSão Paulo State University (UNESP), Av. Profa. Isette Corrêa Fontão, 505, SPUniversidade Estadual Paulista (UNESP)Nascimento Neto, Jayro do [UNESP]Vasconcellos, Rui Marcos Grombone de [UNESP]Ferreira, André Alves [UNESP]2023-03-01T20:01:56Z2023-03-01T20:01:56Z2022-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article690-698http://dx.doi.org/10.1007/s40435-021-00833-0International Journal of Dynamics and Control, v. 10, n. 3, p. 690-698, 2022.2195-26982195-268Xhttp://hdl.handle.net/11449/24011110.1007/s40435-021-00833-02-s2.0-85130635290Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Dynamics and Controlinfo:eu-repo/semantics/openAccess2023-03-01T20:01:56Zoai:repositorio.unesp.br:11449/240111Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:51:59.519808Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Estimating largest Lyapunov exponents in aeroelastic signals
title Estimating largest Lyapunov exponents in aeroelastic signals
spellingShingle Estimating largest Lyapunov exponents in aeroelastic signals
Nascimento Neto, Jayro do [UNESP]
Aeroelastic signals
Chaos
Laypunov exponents
Noise
Nonlinear systems
Singular value decomposition
title_short Estimating largest Lyapunov exponents in aeroelastic signals
title_full Estimating largest Lyapunov exponents in aeroelastic signals
title_fullStr Estimating largest Lyapunov exponents in aeroelastic signals
title_full_unstemmed Estimating largest Lyapunov exponents in aeroelastic signals
title_sort Estimating largest Lyapunov exponents in aeroelastic signals
author Nascimento Neto, Jayro do [UNESP]
author_facet Nascimento Neto, Jayro do [UNESP]
Vasconcellos, Rui Marcos Grombone de [UNESP]
Ferreira, André Alves [UNESP]
author_role author
author2 Vasconcellos, Rui Marcos Grombone de [UNESP]
Ferreira, André Alves [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Nascimento Neto, Jayro do [UNESP]
Vasconcellos, Rui Marcos Grombone de [UNESP]
Ferreira, André Alves [UNESP]
dc.subject.por.fl_str_mv Aeroelastic signals
Chaos
Laypunov exponents
Noise
Nonlinear systems
Singular value decomposition
topic Aeroelastic signals
Chaos
Laypunov exponents
Noise
Nonlinear systems
Singular value decomposition
description The characterization of experimental signals from nonlinear systems is of fundamental importance for the development of precise mathematical models. Aeroelastic systems are intrinsically nonlinear, leading to unpredictable and sometimes uncontrollable situations. Experimental signal analysis still represents the most effective way to characterize most of the phenomena that occur in the different operating conditions of aircraft. Nonlinear signal analysis tools allow the identification and characterization of a nonlinear system, where one of the most important characteristics is the classification of the behavior between stability, instability and the type of instability so that prevention or control techniques can be implemented. However, techniques for characterizing nonlinear behavior, such as determining the largest Lyapunov exponent from the reconstructed state space, can be affected by noise, always present in experimental signals, resulting in an incorrect characterization. In this work, the singular value decomposition method is first applied as state space reconstruction and as a digital filter to the well-known Lorenz attractor at different levels of noise contamination. The effects of noise on the estimation of the largest Lyapunov exponent and the efficiency of the proposed signal filtering technique are discussed. With the procedure verified, the method is applied in experimental aeroelastic signals from a wind tunnel test of a three degrees of freedom aeroelastic wing with freeplay nonlinearity in the control surface. Subsequently, the largest Lyapunov exponent is estimated to characterize the nonlinear behavior.
publishDate 2022
dc.date.none.fl_str_mv 2022-06-01
2023-03-01T20:01:56Z
2023-03-01T20:01:56Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/s40435-021-00833-0
International Journal of Dynamics and Control, v. 10, n. 3, p. 690-698, 2022.
2195-2698
2195-268X
http://hdl.handle.net/11449/240111
10.1007/s40435-021-00833-0
2-s2.0-85130635290
url http://dx.doi.org/10.1007/s40435-021-00833-0
http://hdl.handle.net/11449/240111
identifier_str_mv International Journal of Dynamics and Control, v. 10, n. 3, p. 690-698, 2022.
2195-2698
2195-268X
10.1007/s40435-021-00833-0
2-s2.0-85130635290
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv International Journal of Dynamics and Control
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 690-698
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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