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Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction

dc.contributor.authorRios, Y. Yuliana
dc.contributor.authorGarcía-Rodríguez, J.A
dc.contributor.authorSanchez, Edgar N.
dc.contributor.authorAlanis, Alma Y.
dc.contributor.authorRuiz-Velázquez, E.
dc.contributor.authorPardo Garcia, Aldo
dc.date.accessioned2023-07-19T21:13:05Z
dc.date.available2023-07-19T21:13:05Z
dc.date.issued2022-07
dc.date.submitted2023-07
dc.identifier.citationYuliana Rios, Y., García-Rodríguez, J. A., Sanchez, E. N., Alanis, A. Y., Ruiz-Velázquez, E., & Garcia, A. P. (2022). Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction. ISA Transactions, 126. https://doi.org/10.1016/j.isatra.2021.07.045spa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/12172
dc.description.abstractDiabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific community has directed research in developing technologies to reduce T1DM complications. This contribution is related to a feedback control strategy for blood glucose management in population samples of ten virtual adult subjects, adolescents and children. This scheme focuses on the development of an inverse optimal control (IOC) proposal which is integrated by neural identification, a multi-step prediction (MSP) strategy, and Takagi–Sugeno (T–S) fuzzy inference to shape the convenient insulin infusion in the treatment of T1DM patients. The MSP makes it possible to estimate the glucose dynamics 15 min in advance; therefore, this estimation allows the Neuro-Fuzzy-IOC (NF-IOC) controller to react in advance to prevent hypoglycemic and hyperglycemic events. The T–S fuzzy membership functions are defined in such a way that the respective inferences change basal infusion rates for each patient's condition. The results achieved for scenarios simulated in Uva/Padova virtual software illustrate that this proposal is suitable to maintain blood glucose levels within normoglycemic values (70–115 mg/dL); furthermore, this level remains less than 250 mg/dL during the postprandial event. A comparison between a simple neural IOC (NIOC) and the proposed NF-IOC is carried out using the analysis for control variability named CVGA chart included in the Uva/Padova software. This analysis highlights the improvement of the NF-IOC treatment, proposed in this article, on the NIOC approach because each subject is located inside safe zones for the entire duration of the simulationspa
dc.format.extent10 páginas
dc.format.mediumPdf
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceISA Transactions - Vol. 126spa
dc.titleTreatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step predictionspa
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datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
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dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.hasversioninfo:eu-repo/semantics/draftspa
dc.identifier.doi10.1016/j.isatra.2021.07.045
dc.subject.keywordsRecurrent neural networkspa
dc.subject.keywordsFuzzy inferencespa
dc.subject.keywordsUva/Padova simulatorspa
dc.subject.keywordsNeural multi-step predictorspa
dc.subject.keywordsType 1 Diabetes Mellitusspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa
dc.type.spahttp://purl.org/coar/resource_type/c_6501spa
dc.publisher.sedeCampus Tecnológicospa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_2df8fbb1spa


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