Adaptive control of artificial pancreas systems for treatment of type 1 diabetes

Abstract A personalized multivariable, multimodel artificial pancreas (PMM-AP) system is developed to automate and personalize insulin treatment of type 1 diabetes. The proposed PMM-AP is a fully automated insulin delivery system that works with no meal and physical activity announcements. An adaptive-personalized plasma insulin concentration (PIC) estimator is designed to quantify the amount of active insulin present in the body. Time-varying glycemic models are obtained through a recursive system identification technique using physiological measurements, continuous glucose measurements, estimates of unannounced meal effects and PIC. An adaptive-learning model predictive control algorithm is then designed using the identified glycemic models and PIC estimates to compute a safe and optimal insulin amount. Simulation case studies demonstrate the performance of the proposed PMM-AP system.