Model Predictive Control of blood glucose in Type 1 diabetes: The Principal Dynamic Modes approach

This computational study demonstrates the efficacy of regulating blood glucose in Type 1 diabetics with a Model Predictive Control strategy, utilizing a nonparametric / Principal Dynamic Modes model. For this purpose, a stochastic glucose disturbance signal is introduced and a simple methodology for predicting its future values is developed. The results of our simulations confirm that the proposed algorithm achieves very good performance, is computationally efficient and avoids hypoglycaemic events.

[1]  R.S. Parker,et al.  A model-based algorithm for blood glucose control in Type I diabetic patients , 1999, IEEE Transactions on Biomedical Engineering.

[2]  Robert S. Parker,et al.  Glucose Control in Type I Diabetic Patients: A Volterra Model-Based Approach , 2004 .

[3]  E D Lehmann,et al.  A physiological model of glucose-insulin interaction in type 1 diabetes mellitus. , 1992, Journal of biomedical engineering.

[4]  John Thomas Sorensen,et al.  A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes , 1985 .

[5]  V. Marmarelis.  Modeling methology for nonlinear physiological systems , 1997, Annals of Biomedical Engineering.

[6]  Gary Bradski,et al.  Dynamic programming for optimal control of setup scheduling with neural network modifications , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[7]  R N Bergman,et al.  Diurnal Variation in Glucose Tolerance: Cyclic Suppression of Insulin Action and Insulin Secretion in Normal-Weight, But Not Obese, Subjects , 1992, Diabetes.

[8]  Francis J. Doyle,et al.  Robust H∞ glucose control in diabetes using a physiological model , 2000 .

[9]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[10]  H. Akaike.  A new look at the statistical model identification , 1974 .

[11]  K. Polonsky,et al.  Circadian modulation of glucose and insulin responses to meals: relationship to cortisol rhythm. , 1992, The American journal of physiology.

[12]  Mihalis G. Markakis,et al.  Computational study of an augmented minimal model for glycaemia control , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  R. Hovorka,et al.  Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. , 2004, Physiological measurement.

[14]  Efstratios N. Pistikopoulos,et al.  Model-based blood glucose control for type 1 diabetes via parametric programming , 2006, IEEE Transactions on Biomedical Engineering.

[15]  Francis J. Doyle,et al.  Glucose control design using nonlinearity assessment techniques , 2005 .

[16]  B.W. Bequette,et al.  Model predictive control of blood glucose in type I diabetics using subcutaneous glucose measurements , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[17]  Harold W. Sorenson,et al.  Parameter estimation: Principles and problems , 1980 .

[18]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.