Plasma Insulin Cognizant Predictive Control for Artificial Pancreas

In the present work, an adaptive model predictive control (MPC) algorithm is designed to effectively compute the optimal exogenous insulin delivery for artificial pancreas systems. The proposed MPC is designed using adaptive models that are recursively identified through subspace-based techniques to characterize the transient dynamics of glycemic measurements without requiring any information on the time and amount of carbohydrate consumption. A dynamic safety constraint derived from the estimation of plasma insulin concentration (PIC) is incorporated in the proposed MPC algorithm for the efficacy and reliability of the artificial pancreas system. The MPC algorithm, cognizant of the PIC, computes the optimal control solution to regulate blood glucose concentration while mitigating aggressive control actions (excessive insulin doses) when sufficient insulin is present in the bloodstream, thereby minimizing the risk of hypoglycemia. The efficiency of the proposed MPC algorithm is demonstrated using simulation studies.