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78 Control theory in biomedical engineering

and physiological variables are combined with a recursive subspace-based
system identification approach to obtain the glycemic models. The recur-
sively identified models can describe better the dynamic behavior of
BGC variations in the human body over wide ranges of real-world condi-
tions and make accurate short-term predictions of glucose concentration
measurements. The AL-MPC algorithm developed by using these adaptive
models computes the optimal amount of insulin for AP systems without
requiring any manual information on meal and PA specifications. Using
machine-learning techniques and patients’ historical data, the key control
parameters are modified in advance for the anticipated periods of disturbance
effects such as exercise. The proposed PMM-AP could be a reliable step
toward improved glycemic control by individualizing the insulin computa-
tions and reducing the risk of postexercise hypoglycemia in the next
generation of AP algorithms.

Acknowledgments

This work is supported by the National Institutes of Health under grants 1DP3DK101077-01
and 1DP3DK101075-01, and JDRF award 2-SRA-2017-506-M-B made possible by fund-
ing provided through the collaboration between JDRF and The Leona M. and Harry B.
Helmsley Charitable Trust.

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