Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction. (July 2022)

• Record Type: Journal Article
• Authors: Rios, Y. Yuliana; García-Rodríguez, J.A.; Sanchez, Edgar N.; Alanis, Alma Y.; Ruiz-Velázquez, E.; Pardo Garcia, Aldo
• Is Part Of: ISA transactions. Volume 126(2022)
• Page Start: 203
• Page End: 212
• Publication Date: 2022-07
• Subjects: Recurrent neural network -- Fuzzy inference -- Uva/Padova simulator -- Neural multi-step predictor -- Type 1 Diabetes Mellitus; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.07.045
• Languages: English
• ISSNs: 0019-0578

Abstract

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. Highlights: T1DM treatment using optimal control, neural networks, and T–S fuzzy inferences. Neural identification provides adaptability to different population patients. Neural prediction estimates valuable information to improve closed loop performance. Takagi–Sugeno fuzzy inferences smooth control action values. Variability analysis demonstrates the method relevancy for diabetic children.