Neural Adaptive quantized output-feedback control- based synchronization of uncertain time-delay incommensurate fractionalorder chaotic systems with input nonlinearities

research is concerned with the problem of generalized function projective synchronization of nonlinear uncertain time-delay incommensurate fractional-order chaotic systems with input nonlinearities. The considered problem is challenging owing to the presence of unmeasured master-slave system states,...

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Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad de Bogotá Jorge Tadeo Lozano
Repositorio:
Expeditio: repositorio UTadeo
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OAI Identifier:
oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/9077
Acceso en línea:
http://dx.doi.org/10.1016/j.neucom.2016.11.036
http://hdl.handle.net/20.500.12010/9077
Palabra clave:
Generalized function projective synchronization
Uncertain time-delay chaotic systems
Incommensurate fractional-order systems
Input nonlinearities
Razumikhin Lemma
Frequency distributed model
Adaptive quantized output-feedback control
Caos determinista
Sistemas no lineales
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License
Abierto (Texto Completo)
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oai_identifier_str oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/9077
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repository_id_str
dc.title.spa.fl_str_mv Neural Adaptive quantized output-feedback control- based synchronization of uncertain time-delay incommensurate fractionalorder chaotic systems with input nonlinearities
title Neural Adaptive quantized output-feedback control- based synchronization of uncertain time-delay incommensurate fractionalorder chaotic systems with input nonlinearities
spellingShingle Neural Adaptive quantized output-feedback control- based synchronization of uncertain time-delay incommensurate fractionalorder chaotic systems with input nonlinearities
Generalized function projective synchronization
Uncertain time-delay chaotic systems
Incommensurate fractional-order systems
Input nonlinearities
Razumikhin Lemma
Frequency distributed model
Adaptive quantized output-feedback control
Caos determinista
Sistemas no lineales
title_short Neural Adaptive quantized output-feedback control- based synchronization of uncertain time-delay incommensurate fractionalorder chaotic systems with input nonlinearities
title_full Neural Adaptive quantized output-feedback control- based synchronization of uncertain time-delay incommensurate fractionalorder chaotic systems with input nonlinearities
title_fullStr Neural Adaptive quantized output-feedback control- based synchronization of uncertain time-delay incommensurate fractionalorder chaotic systems with input nonlinearities
title_full_unstemmed Neural Adaptive quantized output-feedback control- based synchronization of uncertain time-delay incommensurate fractionalorder chaotic systems with input nonlinearities
title_sort Neural Adaptive quantized output-feedback control- based synchronization of uncertain time-delay incommensurate fractionalorder chaotic systems with input nonlinearities
dc.subject.spa.fl_str_mv Generalized function projective synchronization
Uncertain time-delay chaotic systems
Incommensurate fractional-order systems
Input nonlinearities
Razumikhin Lemma
Frequency distributed model
Adaptive quantized output-feedback control
topic Generalized function projective synchronization
Uncertain time-delay chaotic systems
Incommensurate fractional-order systems
Input nonlinearities
Razumikhin Lemma
Frequency distributed model
Adaptive quantized output-feedback control
Caos determinista
Sistemas no lineales
dc.subject.lemb.spa.fl_str_mv Caos determinista
Sistemas no lineales
description research is concerned with the problem of generalized function projective synchronization of nonlinear uncertain time-delay incommensurate fractional-order chaotic systems with input nonlinearities. The considered problem is challenging owing to the presence of unmeasured master-slave system states, external dynamical disturbances, unknown nonlinear system functions, unknown time-varying delays, quantized outputs, unknown control direction, unknown actuator nonlinearities (backlash-like hysteresis, dead-zone and asymmetric saturation actuators) and distinct fractional-orders. Under some mild assumptions and using aputo’s definitions for fractional-order integrals and derivatives, the design procedure of the proposed neural adaptive controller consists of a number of steps to solve the generalized function projective synchronization problem. First, smooth functions and the mean value theorem are utilized to overcome the difficulties from actuator nonlinearities and distributed time-varying delays, respectively. Then, a simple linear observer is established to estimate the unknown synchronization error variables. In addition, a Nussbaum function is incorporated to cope with the unknown control direction and a neural network is adopted to tackle the unknown nonlinear functions. The combination of the frequency distributed model, the Razumikhin Lemma, the neural network parameterization, the Lyapunov method and the arbalat’s le a is employed to perform the stability proof of the closed-loop system and to derive the adaption laws. The major advantages of this research are that: (1) the Strictly Positive Real (SPR) condition on the estimation error dynamics is not required, (2) the considered class of master-slave systems is relatively large, (3) all signals in the resulting closed-loop systems are semi-globally uniformly ultimately bounded and the synchronization errors semi-globally converge to zero. Finally, numerical examples are presented to illustrate the performance of the proposed synchronization scheme.
publishDate 2017
dc.date.created.none.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2020-04-29T19:43:39Z
dc.date.available.none.fl_str_mv 2020-04-29T19:43:39Z
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.local.spa.fl_str_mv Artículo
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.identifier.other.spa.fl_str_mv http://dx.doi.org/10.1016/j.neucom.2016.11.036
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12010/9077
dc.identifier.doi.spa.fl_str_mv http://dx.doi.org/10.1016/j.neucom.2016.11.036
dc.identifier.instname.spa.fl_str_mv instname:Universidad de Bogotá Jorge Tadeo Lozano
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional de la Universidad de Bogotá Jorge Tadeo Lozano
url http://dx.doi.org/10.1016/j.neucom.2016.11.036
http://hdl.handle.net/20.500.12010/9077
identifier_str_mv instname:Universidad de Bogotá Jorge Tadeo Lozano
reponame:Repositorio Institucional de la Universidad de Bogotá Jorge Tadeo Lozano
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.local.spa.fl_str_mv Abierto (Texto Completo)
rights_invalid_str_mv Abierto (Texto Completo)
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dc.format.extent.spa.fl_str_mv 72 páginas
dc.format.mimetype.spa.fl_str_mv image/jepg
dc.coverage.spatial.spa.fl_str_mv Bogotá, Colombia
dc.publisher.spa.fl_str_mv Universidad de Bogotá Jorge Tadeo Lozano
institution Universidad de Bogotá Jorge Tadeo Lozano
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spelling Bogotá, Colombia2020-04-29T19:43:39Z2020-04-29T19:43:39Z2017http://dx.doi.org/10.1016/j.neucom.2016.11.036http://hdl.handle.net/20.500.12010/9077http://dx.doi.org/10.1016/j.neucom.2016.11.036instname:Universidad de Bogotá Jorge Tadeo Lozanoreponame:Repositorio Institucional de la Universidad de Bogotá Jorge Tadeo Lozanoresearch is concerned with the problem of generalized function projective synchronization of nonlinear uncertain time-delay incommensurate fractional-order chaotic systems with input nonlinearities. The considered problem is challenging owing to the presence of unmeasured master-slave system states, external dynamical disturbances, unknown nonlinear system functions, unknown time-varying delays, quantized outputs, unknown control direction, unknown actuator nonlinearities (backlash-like hysteresis, dead-zone and asymmetric saturation actuators) and distinct fractional-orders. Under some mild assumptions and using aputo’s definitions for fractional-order integrals and derivatives, the design procedure of the proposed neural adaptive controller consists of a number of steps to solve the generalized function projective synchronization problem. First, smooth functions and the mean value theorem are utilized to overcome the difficulties from actuator nonlinearities and distributed time-varying delays, respectively. Then, a simple linear observer is established to estimate the unknown synchronization error variables. In addition, a Nussbaum function is incorporated to cope with the unknown control direction and a neural network is adopted to tackle the unknown nonlinear functions. The combination of the frequency distributed model, the Razumikhin Lemma, the neural network parameterization, the Lyapunov method and the arbalat’s le a is employed to perform the stability proof of the closed-loop system and to derive the adaption laws. The major advantages of this research are that: (1) the Strictly Positive Real (SPR) condition on the estimation error dynamics is not required, (2) the considered class of master-slave systems is relatively large, (3) all signals in the resulting closed-loop systems are semi-globally uniformly ultimately bounded and the synchronization errors semi-globally converge to zero. Finally, numerical examples are presented to illustrate the performance of the proposed synchronization scheme.72 páginasimage/jepgUniversidad de Bogotá Jorge Tadeo LozanoGeneralized function projective synchronizationUncertain time-delay chaotic systemsIncommensurate fractional-order systemsInput nonlinearitiesRazumikhin LemmaFrequency distributed modelAdaptive quantized output-feedback controlCaos deterministaSistemas no linealesNeural Adaptive quantized output-feedback control- based synchronization of uncertain time-delay incommensurate fractionalorder chaotic systems with input nonlinearitiesArtículoinfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_2df8fbb1Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2Zouari, FaroukBoulkroune, AbdesselemIbeas, AsierLICENSElicense.txtlicense.txttext/plain; charset=utf-82938https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/9077/2/license.txtabceeb1c943c50d3343516f9dbfc110fMD52open accessTHUMBNAILCaptura.PNGCaptura.PNGVer portadaimage/png49016https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/9077/4/Captura.PNG971b8dd02aa45b37c6a169157288a1cbMD54open access9465.pdf.jpg9465.pdf.jpgIM Thumbnailimage/jpeg10004https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/9077/5/9465.pdf.jpg4ff73555a15553e3ea3053e1579ac7c7MD55open accessORIGINALCaptura.PNGCaptura.PNGVer portadaimage/png49016https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/9077/1/Captura.PNG971b8dd02aa45b37c6a169157288a1cbMD51open access9465.pdf9465.pdfArtículo reservadoapplication/pdf1496714https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/9077/3/9465.pdf751108f65b041d423fe65675f4a05077MD53embargoed access|||2200-04-2920.500.12010/9077oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/90772020-04-29 14:45:59.314open accessRepositorio Institucional - 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