Simultaneous model predictive control and moving horizon estimation for blood glucose regulation in Type 1 diabetes

A new estimation and control approach for the feedback control of an artificial pancreas to treat type 1 diabetes mellitus is proposed. In particular, we present a new output feedback predictive control approach that simultaneously solves the state estimation and control objectives by means of a single min-max optimization problem. This involves optimizing a cost function with both finite forward and backward horizons with respect to the unknown initial state, unmeasured disturbances and noise, and future control inputs, and is similar to simultaneously solving a Model Predictive Control (MPC) problem and a Moving Horizon Estimation (MHE) problem. We incorporate a novel asymmetric output cost in order to penalize dangerous low blood-glucose values more severely than less harmful high blood-glucose values. We compare this combined MPC/MHE approach to a control strategy that uses state-feedback MPC preceded by a Luenberger observer for state estimation. In-silico results showcase several advantages of this new simultaneous MPC/MHE approach, including fewer hypoglycemic events without increasing the number of hyperglycemic events, faster insulin delivery in response to meal consumption, and shorter insulin pump suspensions, resulting in smoother blood-glucose trajectories. Copyright c © 2016 John Wiley & Sons, Ltd.

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