Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor

Autores
Rómoli, Santiago; Serrano, Mario Emanuel; Rossomando, Francisco Guido; Vega, Jorge Ruben; Ortiz, Oscar; Scaglia, Gustavo Juan Eduardo
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.
Fil: Rómoli, Santiago. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Serrano, Mario Emanuel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Ortiz, Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Neural Network Estimator
Control System Design
Linear Algebra
Integral Action
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/85192
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/85192
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactorRómoli, SantiagoSerrano, Mario EmanuelRossomando, Francisco GuidoVega, Jorge RubenOrtiz, OscarScaglia, Gustavo Juan EduardoNeural Network EstimatorControl System DesignLinear AlgebraIntegral Actionhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.Fil: Rómoli, Santiago. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Serrano, Mario Emanuel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Ortiz, Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaHindawi Publishing Corporation2017-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/85192Rómoli, Santiago; Serrano, Mario Emanuel; Rossomando, Francisco Guido; Vega, Jorge Ruben; Ortiz, Oscar; et al.; Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor; Hindawi Publishing Corporation; Complexity; 2017; 7-2017; 1-16; 93918791076-27871099-0526CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1155/2017/9391879info:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/complexity/2017/9391879/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-17T11:09:13Zoai:ri.conicet.gov.ar:11336/85192instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-17 11:09:14.297CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor
title Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor
spellingShingle Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor
Rómoli, Santiago
Neural Network Estimator
Control System Design
Linear Algebra
Integral Action
title_short Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor
title_full Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor
title_fullStr Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor
title_full_unstemmed Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor
title_sort Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor
dc.creator.none.fl_str_mv Rómoli, Santiago
Serrano, Mario Emanuel
Rossomando, Francisco Guido
Vega, Jorge Ruben
Ortiz, Oscar
Scaglia, Gustavo Juan Eduardo
author Rómoli, Santiago
author_facet Rómoli, Santiago
Serrano, Mario Emanuel
Rossomando, Francisco Guido
Vega, Jorge Ruben
Ortiz, Oscar
Scaglia, Gustavo Juan Eduardo
author_role author
author2 Serrano, Mario Emanuel
Rossomando, Francisco Guido
Vega, Jorge Ruben
Ortiz, Oscar
Scaglia, Gustavo Juan Eduardo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Neural Network Estimator
Control System Design
Linear Algebra
Integral Action
topic Neural Network Estimator
Control System Design
Linear Algebra
Integral Action
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.
Fil: Rómoli, Santiago. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Serrano, Mario Emanuel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Ortiz, Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina
Fil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.
publishDate 2017
dc.date.none.fl_str_mv 2017-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/85192
Rómoli, Santiago; Serrano, Mario Emanuel; Rossomando, Francisco Guido; Vega, Jorge Ruben; Ortiz, Oscar; et al.; Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor; Hindawi Publishing Corporation; Complexity; 2017; 7-2017; 1-16; 9391879
1076-2787
1099-0526
CONICET Digital
CONICET
url http://hdl.handle.net/11336/85192
identifier_str_mv Rómoli, Santiago; Serrano, Mario Emanuel; Rossomando, Francisco Guido; Vega, Jorge Ruben; Ortiz, Oscar; et al.; Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor; Hindawi Publishing Corporation; Complexity; 2017; 7-2017; 1-16; 9391879
1076-2787
1099-0526
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1155/2017/9391879
info:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/complexity/2017/9391879/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Hindawi Publishing Corporation
publisher.none.fl_str_mv Hindawi Publishing Corporation
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1843606420334837760
score 13.001348

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