A Learning Strategy for the Autonomous Control of Type 1 Diabetes

Abstract

This article proposes a learning strategy for the control of the blood glucose in type 1 diabetes based on continuous subcutaneous glucose measurement and subcutaneous insulin administration. The method relies on an Iterative Learning Control strategy that exploits the approximated repetitiveness of the daily feeding habits of a patient. The administration strategy for the insulin is based on a mixed feedback and feedforward law whose parameters are tuned through a learning process based on the day-by-day analysis of the glucose response to the infusion of exogenous insulin. The proposed scheme is fully autonomous in the sense that it does not require any a priori information on the insulin/glucose response of the patient, on the amount of ingested carbohydrates, and on the announcement of the mealtimes. A novel filtering strategy of the subcutaneous glucose signal is proposed to provide a robust detection of the meal occurrence despite the significant noise introduced by the subcutaneous glucose sensor. A specific module is proposed to detect and prevent possible hypoglycemia events. Considering a prototype diabetic virtual patient it was showed that, thanks to the learning mechanism, the scheme in a few days is able to bring and to maintain the blood glucose in the normoglycemia region and that the control performance can improve over time. Long-run simulation studies have also shown the robustness of the learning scheme in the presence of realistic uncertainties and interpatient variability.