Comparing individualization strategies of Model Predictive Control for Artificial Pancreas

Personalization is a key in developing technologies for Type 1 Diabetes (T1D) management, given the large inter- and intra-variability in patients response. This holds in the design of Artificial Pancreas (AP) systems as well. Model Predictive Control (MPC) is one of the most adopted control strategies for this purpose. Leveraging a model of glucose-insulin dynamics, MPC proactively adjusts insulin infusion based on the predicted impact of this action on glucose concentration level. Personalization strategies in MPC could be based on the use of individual-specific models and/or rely on the customization of the cost function. A comparison of these approaches seems to be missing in literature. Therefore, this work investigates three levels of individualization (of cost only, model only, and both cost and model) for a MPC-based AP system. Their comparison is performed using the UVa/Padova T1D Simulator (v.S2014). The latter two strategies are found to significantly outperform the former, provided that the adopted individualized model is compliant with basic physiological requirements.