  This work presents a Model Predictive Control (MPC) for the artificial
pancreas, which is able to autonomously manage basal insulin injections in type
1 diabetic patients. Specifically, the MPC goal is to maintain the patients'
blood glucose level inside the safe range of 70-180 mg/dL, acting on the
insulin amount and respecting all the imposed constraints, taking into
consideration also the Insulin On Board (IOB), to avoid excess of insulin
infusion. MPC uses a model to make predictions of the system behaviour. In this
work, due to the complexity of the diabetes disease that complicates the
identification of a general physiological model, a data-driven learning method
is employed instead. The Componentwise H\"{o}lder Kinky Inference (CHoKI)
method is adopted, to have a customized controller for each patient. For the
data collection phase and also to test the proposed controller, the virtual
patients of the FDA-accepted UVA/Padova simulator are exploited. The proposed
MPC is also tested on a modified version of the simulator, that takes into
consideration also the variability of the insulin sensitivity. The final
results are satisfying since the proposed controller reduces the time in
hypoglycemia (which is more dangerous) if compared to the outcome obtained with
the standard constant basal insulin therapy provided by the simulator,
satisfying also the time in range requirements and avoiding long-term
hyperglycemia events.
