Embedded Model Predictive Control for a Wearable Artificial Pancreas

While artificial pancreas (AP) systems are expected to improve the quality of life among people with type 1 diabetes mellitus (T1DM), the design of convenient systems that optimize the user experience, especially for those with active lifestyles, such as children and adolescents, still remains an open research question. In this brief, we introduce an embeddable design and implementation of model predictive control (MPC) of AP systems for people with T1DM that significantly reduces the weight and on-body footprint of the AP system. The embeddable controller is based on a zone MPC (ZMPC) that has been evaluated in multiple clinical studies. The proposed embedded ZMPC features a simpler design of the periodic safe zone in the cost function and the utilization of state-of-the-art alternating minimization algorithms for solving the convex programing problems inherent to MPC with linear models subject to convex constraints. Offline closed-loop data generated by the FDA-accepted UVA/Padova simulator are used to select an optimization algorithm and the corresponding tuning parameters. Through hardware-in-the-loop in silico results on a limited-resource Arduino Zero (Feather M0) platform, we demonstrate the potential of the proposed embedded MPC. In spite of resource limitations, our embedded ZMPC manages to achieve a comparable performance of that of the full-version ZMPC implemented in a 64-bit desktop for scenarios with/without meal-disturbance compensations. Metrics for performance comparison included the median percent time in the euglycemic ([70, 180] mg/dL range) of 84.3% versus 83.1% for announced meals, with an equivalence test yielding $p=0.0013$ and 66.2% versus 66.0% for unannounced meals with $p=0.0028$.