MPC based Artificial Pancreas: Strategies for individualization and meal compensation

This paper addresses the design of glucose regulators based on Model Predictive Control (MPC) to be used as part of Artificial Pancreas devices for type 1 diabetic patients. Two key issues are deeply investigated: individualization, needed to cope with intersubject variability, and meal compensation, interpreted as a disturbance rejection problem. The individualization is achieved either by tuning the cost function, based on few well known clinical parameters (MPC1) or through the use of an individual model obtained via system identification techniques and an optimal tuning of the cost function based on real-life experiments (MPC2). The in silico tests, performed on 4 different scenarios using a simulator equipped with 100 patients, show that the performances of MPC1 are very promising, supporting its current use in an in vivo multicenter trial on 47 patients that is being carried out within the European Research Project AP@home. At the same time, further improvements are achieved by MPC2, showing that there is scope for in vivo experimentation of control strategies employing individually estimated patient models.