An augmented, adaptive algorithm utilizing model predictive control (MPC) is developed for closed-loop glucose control in type 1 diabetes. A linear empirical input-output subject model is used with an MPC algorithm to regulate blood glucose online, where the subject model is recursively adapted, and the control signal for delivery of insulin and a counter-regulatory agent such as glucagon is based solely on online glucose concentration measurements. The MPC signal is synthesized by optimizing an augmented objective function that minimizes local insulin accumulation in the subcutaneous depot and control signal aggressiveness, while simultaneously regulating glucose concentration to a preset reference set point. The mathematical formulation governing the subcutaneous accumulation of administered insulin is derived based on nominal temporal values pertaining to the pharmacokinetics (time-course of activity) of insulin in human, in terms of its absorption rate, peak absorption time, and overall time of action. The MPC algorithm is also formulated to provide control action with an integral effect, and in essence minimizes overall drug consumption. When employed as a modulator in an automated integrated glucose-control system for type 1 diabetes, the control algorithm provides the system with self-learning capability that enables it to operate under unrestricted activity of the subject.
