Documents
Rios, Y.Y.a , García-Rodríguez, J.A.b , Sanchez, E.N.c , Alanis, A.Y.b , Ruiz-Velázquez, E.b , Pardo Garcia, A.d
Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step
prediction
(2022) ISA Transactions, 126, pp. 203-212. Cited 2 times.
DOI: 10.1016/j.isatra.2021.07.045
a GAICO, Grupo de Automatización y Control, Universidad Tecnológica de Bolívar, Cartagena de Indias,
Bolívar, Colombia
b CUCEI, Electronics and Computing Division, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
c CINVESTAV, Electrical Engineering Department, Zapopan, Jalisco, Mexico
d A&C, Grupo de Automatización y Control, Universidad de Pamplona, Pamplona, Norte de Santander,
Colombia
Abstract
Diabetes 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 simulation. © 2021 ISA
Index Keywords
Blood, Controllers, Fuzzy neural networks, Glucose, Membership functions, Patient treatment; Blood glucose
management, Feedback control strategies, Fuzzy membership function, Inverse-optimal control, Multi-step
prediction, Neural identification, Scientific community, Type 1 diabetes mellitus; Fuzzy inference; antidiabetic
agent, insulin; adolescent, adult, algorithm, child, computer simulation, glucose blood level, human, insulin
dependent diabetes mellitus; Adolescent, Adult, Algorithms, Blood Glucose, Child, Computer
Simulation, Diabetes Mellitus, Type 1, Humans, Hypoglycemic Agents, Insulin
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Document Type: Article
Publication Stage: Final
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