An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes.
This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.
['Fravolini|M L|ML|', 'Fabietti|P G|PG|']
D000465:Algorithms D001786:Blood Glucose D002241:Carbohydrates D003198:Computer Simulation D003922:Diabetes Mellitus, Type 1 D025461:Feedback, Physiological D005247:Feeding Behavior D006801:Humans D007004:Hypoglycemic Agents D007328:Insulin D008875:Middle Aged
feedback control type-1 diabetes artificial pancreas iterative learning control physiological model