Model predictive control for type 1 diabetes based on personalized linear time-varying subject model consisting of both insulin and meal inputs: In Silico evaluation

An essential component of insulin therapy for type 1 diabetes involves the prediction of blood glucose levels as function of exogenous perturbations such as insulin dose and meal intake. Based on the authors´ previously developed patient-specific linear time-varying model for glucose dynamics consisting of both insulin and meal inputs, this paper develops a model predictive control for determining the optimal insulin delivery to regulate blood glucose in euglycemic range. Evaluation of the developed controller using the UVa/Padova simulator shows promising results. In addition, results in this paper show the importance of explicitly including the meal intake in the regression model, which was often lacking in the existing empirical subject-model based control.