Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control‐based approach

Here, a direct adaptive control strategy with parametric compensation is adopted for an uncertain non-linear model representing blood glucose regulation in type 1 diabetes mellitus patients. The uncertain parameters of the model are updated by appropriate design of adaptation laws using the Lyapunov method. The closed-loop response of the plasma glucose concentration as well as external insulin infusion rate is analysed for a wide range of variation of the model parameters through extensive simulation studies. The result indicates that the proposed adaptive control scheme avoids severe hypoglycaemia and gives satisfactory performance under parametric uncertainty highlighting its ability to address the issue of inter-patient variability.

[1]  R. Bergman,et al.  Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. , 1981, The Journal of clinical investigation.

[2]  Hui Yang,et al.  A direct MRAC based multivariable multiple-model switching control scheme , 2017, Autom..

[3]  Gang Tao,et al.  Multivariable adaptive control: A survey , 2014, Autom..

[4]  M. Fisher,et al.  A semiclosed-loop algorithm for the control of blood glucose levels in diabetics , 1991, IEEE Transactions on Biomedical Engineering.

[5]  S. Mahmoud,et al.  Adaptive control of the human glucose-regulatory system , 2006, Medical and biological engineering.

[6]  Stephen D Patek,et al.  Linear Quadratic Gaussian-Based Closed-Loop Control of Type 1 Diabetes , 2007, Journal of diabetes science and technology.

[7]  Giovanni Sparacino,et al.  Diabetes: Models, Signals, and Control , 2009 .

[8]  Y. Batmani Blood glucose concentration control for type 1 diabetic patients: a non-linear suboptimal approach. , 2017, IET systems biology.

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

[10]  Ahmad Haidar,et al.  The Artificial Pancreas: How Closed-Loop Control Is Revolutionizing Diabetes , 2016, IEEE Control Systems.

[11]  N. Paquot,et al.  Measurement of insulin sensitivity by the minimal model method using a simplified intravenous glucose tolerance test: validity and reproducibility. , 1994, Diabete & metabolisme.

[12]  Claudio Cobelli,et al.  The Oral Minimal Model Method , 2014, Diabetes.

[13]  J F Casanova Domingo,et al.  Adaptation drift suppression in blood glucose self-tuning control. , 1997, Artificial organs.

[14]  S. Coman,et al.  SIMULATION OF AN ADAPTIVE CLOSED LOOP SYSTEM FOR BLOOD GLUCOSE CONCENTRATION CONTROL , 2015 .

[15]  B. Saboo,et al.  Continuous subcutaneous insulin infusion: practical issues , 2012, Indian journal of endocrinology and metabolism.

[16]  G. P. Rangaiah,et al.  Review and Analysis of Blood Glucose (BG) Models for Type 1 Diabetic Patients , 2011 .

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

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

[19]  W. Schenk,et al.  Does Physiological Blood Glucose Control Require an Adaptive Control Strategy? , 1987, IEEE Transactions on Biomedical Engineering.

[20]  Gang Tao,et al.  Parameterization and Adaptive Control of Multivariable Noncanonical T--S Fuzzy Systems , 2017, IEEE Transactions on Fuzzy Systems.

[21]  Niels Kjølstad Poulsen,et al.  Adaptive control in an artificial pancreas for people with type 1 diabetes , 2017 .

[22]  F. El-Khatib,et al.  Adaptive Closed-Loop Control Provides Blood-Glucose Regulation Using Dual Subcutaneous Insulin and Glucagon Infusion in Diabetic Swine , 2007, Journal of diabetes science and technology.

[23]  Ján Murgaš,et al.  Model Reference Adaptive Control of Glucose in Type 1 Diabetics: A Simulation Study , 2014 .

[24]  Ali Cinar,et al.  Multivariable Adaptive Identification and Control for Artificial Pancreas Systems , 2014, IEEE Transactions on Biomedical Engineering.

[25]  Gang Tao,et al.  Adaptive compensation control of synthetic jet actuator arrays for airfoil virtual shaping , 2007 .

[26]  Gang Tao,et al.  Adaptive Synthetic Jet Actuator Compensation for A Nonlinear Aircraft Model at Low Angles of Attack , 2008, IEEE Transactions on Control Systems Technology.

[27]  Claudio Cobelli,et al.  Individually Adaptive Artificial Pancreas in Subjects with Type 1 Diabetes: A One-Month Proof-of-Concept Trial in Free-Living Conditions , 2017 .

[28]  Ali Cinar,et al.  Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes , 2009 .

[29]  Hyunjin Lee,et al.  A closed-loop artificial pancreas based on model predictive control: Human-friendly identification and automatic meal disturbance rejection , 2009, Biomed. Signal Process. Control..

[30]  Z. Tashakorizade,et al.  Glucose Regulation in Type 1 Diabetes Mellitus with Model Reference Adaptive Control and Modified Smith Predictor , 2016 .

[31]  C. Cobelli,et al.  Artificial Pancreas: Past, Present, Future , 2011, Diabetes.