Periodic MPC for glucose control in Type 1 Diabetes

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.