Model reference adaptive advisory system based on sparse measurements for insulin adjustment in diabetes mellitus

This paper describes a model reference adaptive system which assists in insulin therapy adjustment. In contrast to most of the similar adaptive blood glucose controllers which need a relative high frequency of blood glucose measurement (2–30 min⁻¹), this adaptive optimizer can operate with a feasible daily number (4–5) of blood samples taken from the diabetic patient. In the absence of real data, a reference model behaving as a X "healthy subject" generates "pseudo" blood glucose values. Also, the proposed scheme makes use of the knowledge of the time and quantity of future meals. On the basis of numerical simulations, the evaluation of which are in progress, it is expected that the combined adaptive and preview optimization scheme will significantly improve the performence of the blood glucose control system.