Work

Periodic MPC for glucose control in Type 1 Diabetes

Year: 2025

Type: article

Abstract: Glucose control in Type 1 Diabetes (T1D) is a challenging task due to several factors among which intra- and inter-patient variability play a key role. This work introduces a Periodic Multiple Model Predictor (PeMMP) integrated in a Periodic Model Predictive Control (PeMPC) to enhance glucose regulation. The PeMMP is able to account for time-varying dynamics by switching smoothly between multiple models. In a case study including a single patient of the UVA/Padova simulator, the PeMMP improves prediction accuracy compared to the traditional Daily Model Predictor (DMP) (fitting index 81.9% vs 39.7%). These more accurate predictions lead to an improvement in control performances when exploited in a PeMPC especially in the postprandial periods increasing the time in range (93.45% to 96.31%) and decreasing hypoglycemia (1.42% to 0%) and hypo treatments (10 to 0). These results highlight PeMPC as a promising approach for better managing T1D, its use on the entire in silico population is currently under study.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.06.005

Authors: Paolo A. Mongini, Matteo Ragni, Lalo Magni, Chiara Toffanin

Institution: University of Pavia

Language: English

Topic: Diabetes Management and Research

Subfield: Endocrinology, Diabetes and Metabolism

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: European Commission, Ministero dell’Istruzione, dell’Università e della Ricerca, Horizon 2020 Framework Programme