Model predictive control of glucose concentration in type I diabetic patients: An in silico trial

Abstract In this paper, the feedback control of glucose concentration in type I diabetic patients using subcutaneous insulin delivery and subcutaneous continuous glucose monitoring is considered. A recently developed in silico model of glucose metabolism is employed to generate virtual patients on which control algorithms can be validated against interindividual variability. An in silico trial consisting of 100 patients is used to assess the performances of a linear output feedback and a nonlinear state-feedback model predictive controller, designed on the basis of the in silico model. More than satisfactory results are obtained in the great majority of virtual patients. The experiments highlight the crucial role of the anticipative feedforward action driven by the meal announcement information. Preliminary results indicate that further improvements may be achieved by means of a nonlinear model predictive control scheme.

[1]  Riccardo Scattolini,et al.  Model predictive control of continuous-time nonlinear systems with piecewise constant control , 2004, IEEE Transactions on Automatic Control.

[2]  Claudio Cobelli,et al.  Meal Simulation Model of the Glucose-Insulin System , 2007, IEEE Transactions on Biomedical Engineering.

[3]  B. Bequette A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. , 2005, Diabetes technology & therapeutics.

[4]  A H Clemens,et al.  The development of Biostator, a Glucose Controlled Insulin Infusion System (GCIIS). , 1977, Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme.

[5]  R. Hovorka Continuous glucose monitoring and closed‐loop systems , 2006, Diabetic medicine : a journal of the British Diabetic Association.

[6]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[7]  Y. Z. Ider,et al.  Quantitative estimation of insulin sensitivity. , 1979, The American journal of physiology.

[8]  William L Clarke,et al.  Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application. , 2005, Diabetes technology & therapeutics.

[9]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

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

[11]  L. Magni,et al.  Evaluating the Efficacy of Closed-Loop Glucose Regulation via Control-Variability Grid Analysis , 2008, Journal of diabetes science and technology.

[12]  L. Magni,et al.  Model Predictive Control of Type 1 Diabetes: An in Silico Trial , 2007, Journal of diabetes science and technology.

[13]  G. Steil,et al.  Evaluation of the Effect of Gain on the Meal Response of an Automated Closed-Loop Insulin Delivery System , 2006, Diabetes.

[14]  David C Klonoff,et al.  The Artificial Pancreas: How Sweet Engineering Will Solve Bitter Problems , 2007, Journal of diabetes science and technology.

[15]  R. Rizza,et al.  Effects of Plasma Glucose Concentration on Glucose Utilization and Glucose Clearance in Normal Man , 1981, Diabetes.

[16]  Eduardo F. Camacho,et al.  Model Predictive Controllers , 2007 .