Personalized Constrained MPC for glucose regulation

Abstract

The technological advances reached in the last years overcome the relevant hardware limitations of the past artificial pancreas releasing portable devices with high computational capabilities. In view of this, the use of a Constrained MPC (CMPC) that needs the solution of a finite horizon optimal control problem can be considered. The real advantage of the explicit inclusion of the state constraints in the optimization problem is mainly related to the quality of the model used for the predictions. In a recent work, a CMPC based on an average model has been tested on the 100 in silico patients of the new UVA/Padova simulator, showing satisfactory results in terms of average glucose, time spent in tight range and time above 180 mg/dl. A percentage of time below 70 mg/dl grater than 3% is present for more than 25% of the patients. In this work, a new personalized CMPC is proposed to mitigate the hypoglycemia phenomena for these patients, using personalized impulse-response models for glucose prediction.