Glucose dynamics in type 1 diabetes are highly variable both across individuals and within the same individual throughout the day, due to factors such as meals, insulin sensitivity, and daily routines. This variability poses significant challenges for accurate prediction and control, limiting the effectiveness of single-model approaches. The aim of this work is to develop control-oriented models to provide accurate predictions of glucose dynamics, to be used within control strategies, such as model predictive control. The proposed approach adopts a periodic structure, based on multiple models, that accounts for both individual differences, in terms of metabolic response, and daily fluctuations in patient dynamics. Models are identified in different daily periods using an impulse response method applied to data generated by the UVA/Padova simulator. The day is divided into three time segments corresponding to breakfast, lunch, and dinner, with a separate model trained for each period. These models are integrated through a soft-switching mechanism. Two state estimation techniques, the Kalman filter and the moving horizon estimator, are compared to enable multi-step glucose prediction. Results indicate that the periodic predictor consistently outperforms the invariant predictor approaches across the entire virtual adult population. This modeling framework shows strong potential for integration into advanced insulin delivery systems based on predictive control. • Periodic structure improves blood glucose prediction in type 1 diabetes. • Model tailored for different times of the day reflect daily metabolic variability. • Soft-switching ensures smooth transitions between time-specific glucose models. • Two estimators are compared for accurate blood glucose forecasting.
Ragni et al. (Sun,) studied this question.