Duun-Henriksen, Anne Katrine7; Boiroux, Dimitri8; Schmidt, Signe9; Skyggebjerg, Ole10; Madsbad, Sten9; Jensen, Peter Ruhdal11; Jørgensen, John Bagterp12; Poulsen, Niels Kjølstad8; Nørgaard, Kirsten9; Madsen, Henrik8
1 Department of Informatics and Mathematical Modeling, Technical University of Denmark2 Scientific Computing, Department of Informatics and Mathematical Modeling, Technical University of Denmark3 Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark4 Department of Systems Biology, Technical University of Denmark5 Center for Systems Microbiology, Department of Systems Biology, Technical University of Denmark6 Center for Energy Resources Engineering, Center, Technical University of Denmark7 Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark8 Department of Applied Mathematics and Computer Science, Technical University of Denmark9 Copenhagen University Hospital10 Horus ApS11 Systems Biotechnology, Department of Systems Biology, Technical University of Denmark12 Copenhagen Center for Health Technology, Center, Technical University of Denmark
People with type 1 diabetes need several insulin injections every day to keep their blood glucose level in the normal range and thereby avoiding the acute and long term complications of diabetes. One of the recent treatments consists of a pump injecting insulin into the subcutaneous layer combined with a continuous glucose monitor (CGM) frequently observing the glucose level. Automatic control of the insulin pump based on CGM observations would ease the burden of constant diabetes treatment and management. We have developed a controller designed to keep the blood glucose level in the normal range by adjusting the size of insulin infusions from the pump based on model predictive control (MPC). A clinical pilot study to test the performance of the MPC controller overnight was performed. The conclusion was that the controller relied too much on the local trend of the blood glucose level which is a problem due to the noise corrupted observations from the CGM. In this paper we present a method to estimate the optimal Kalman gain in the controller based on stochastic differential equation modeling. With this model type we could estimate the process noise and observation noise separately based on data from the rst clinical pilot study. In doing so we obtained a more robust control algorithm which is less sensitive to fluctuations in the CGM observations and rely more on the global physiological trend of the blood glucose level. Finally, we present the promising results from the second pilot study testing the improved controller.
Biological and Medical Systems, 2012, p. 46-51
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8th IFAC Symposium on Biological and Medical Systems, 2012