Optimal Experimental Design for System Identification in a Bi-Hormonal Intraperitoneal Artificial Pancreas

For individuals with diabetes, the intraperitoneal drug-delivery route may enable fully automated artificial pancreas technology. For such systems, the model predictive control (MPC) algorithm is favorable. However, MPC requires a reliable predictive model. In this work, we aim to design a trial protocol to collect data for identification of a bi-hormonal intraperitoneal prediction model. We apply model-based design of experiment (MBDoE) to determine the optimal input of meals, subcutaneous insulin injections, and subcutaneous glucagon injections. Based on parameters from two anesthetized pigs, we design experiments to identify parameters in awake animals. Our results demonstrate how MBDoE may be used as a planning tool when designing trial protocols. The approach may hold potential as a support tool for clinicians when personalizing control algorithms for human AP users.