State Estimation with Sensor Recalibrations and Asynchronous Measurements for MPC of an Artificial Pancreas to Treat T1DM

A novel state estimation scheme is proposed for use in Model Predictive Control (MPC) of an artificial pancreas based on Continuous Glucose Monitor (CGM) feedback, for treating type 1 diabetes mellitus. The performance of MPC strategies heavily depends on the initial condition of the predictions, typically characterized by a state estimator. Commonly employed Luenberger-observers and Kalman-filters are effective much of the time, but suffer limitations. Three particular limitations are tackled by the proposed approach. First, CGM recalibrations, step changes that cause highly dynamic responses in recursive state estimators, are accommodated in a graceful manner. Second, the proposed strategy is not affected by CGM measurements that are asynchronous, i.e., neither of fixed sample-period, nor of a sample-period that is equal to the controller's. Third, the proposal suffers no offsets due to plant-model mismatches. The proposed approach is based on moving-horizon optimization.