  The human insulin-glucose metabolism is a time-varying process, which is
partly caused by the changing insulin sensitivity of the body. This insulin
sensitivity follows a circadian rhythm and its effects should be anticipated by
any automated insulin delivery system. This paper presents an extension of our
previous work on automated insulin delivery by developing a controller suitable
for humans with Type 1 Diabetes Mellitus. Furthermore, we enhance the
controller with a new kernel function for the Gaussian Process and deal with
noisy measurements, as well as, the noisy training data for the Gaussian
Process, arising therefrom. This enables us to move the proposed control
algorithm, a combination of Model Predictive Controller and a Gaussian Process,
closer towards clinical application. Simulation results on the University of
Virginia/Padova FDA-accepted metabolic simulator are presented for a meal
schedule with random carbohydrate sizes and random times of carbohydrate uptake
to show the performance of the proposed control scheme.
