  We present a fully closed-loop design for an artificial pancreas (AP) which
regulates the delivery of insulin for the control of Type I diabetes. Our AP
controller operates in a fully automated fashion, without requiring any manual
interaction (e.g. in the form of meal announcements) with the patient. A major
obstacle to achieving closed-loop insulin control is the uncertainty in those
aspects of a patient's daily behavior that significantly affect blood glucose,
especially in relation to meals and physical activity. To handle such
uncertainties, we develop a data-driven robust model-predictive control
framework, where we capture a wide range of individual meal and exercise
patterns using uncertainty sets learned from historical data. These sets are
then used in the controller and state estimator to achieve automated, precise,
and personalized insulin therapy. We provide an extensive in silico evaluation
of our robust AP design, demonstrating the potential of this approach, without
explicit meal announcements, to support high carbohydrate disturbances and to
regulate glucose levels in large clusters of virtual patients learned from
population-wide survey data.
