Velocity-weighting to prevent controller-induced hypoglycemia in MPC of an artificial pancreas to treat T1DM

The design of a Model Predictive Control (MPC) strategy for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes mellitus is considered. The contribution of this paper is to propose a velocity-weighting mechanism, within an MPC problem's cost function, that facilitates penalizing predicted hyperglycemic blood-glucose excursions based on the predicted blood-glucose levels' rates of change. The method provides the control designer some freedom for independently shaping the AP's uphill versus downhill responses to hyperglycemic excursions; of particular emphasis in this paper is the downhill response. The proposal aims to tackle the dangerous issue of controller-induced hypoglycemia following large hyperglycemic excursions, e.g., after meals, that results in part due to the large delays of subcutaneous glucose sensing and subcutaneous insulin infusion - the case considered here. The efficacy of the proposed approach is demonstrated using the University of Virginia/Padova metabolic simulator with both unannounced and announced meal scenarios.

[1]  Giuseppe De Nicolao,et al.  Model predictive control of glucose concentration in type I diabetic patients: An in silico trial , 2009, Biomed. Signal Process. Control..

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

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

[4]  Howard C. Zisser,et al.  A Feedforward-Feedback Glucose Control Strategy for Type 1 Diabetes Mellitus. , 2008, Journal of process control.

[5]  Howard Zisser,et al.  Clinical Hurdles and Possible Solutions in the Implementation of Closed-Loop Control in Type 1 Diabetes Mellitus , 2011, Journal of diabetes science and technology.

[6]  C. Cobelli,et al.  In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes , 2009, Journal of diabetes science and technology.

[7]  William S. Levine,et al.  The Control Handbook , 2005 .

[8]  Eyal Dassau,et al.  Periodic-Zone Model Predictive Control for Diurnal Closed-Loop Operation of an Artificial Pancreas , 2013, Journal of diabetes science and technology.

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

[10]  Eyal Dassau,et al.  MPC design for rapid pump-attenuation and expedited hyperglycemia response to treat T1DM with an Artificial Pancreas , 2014, 2014 American Control Conference.

[11]  Eyal Dassau,et al.  Zone Model Predictive Control: A Strategy to Minimize Hyper- and Hypoglycemic Events , 2010, Journal of diabetes science and technology.

[12]  K. Turksoy,et al.  Multivariable adaptive closed-loop control of an artificial pancreas without meal and activity announcement. , 2013, Diabetes technology & therapeutics.

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

[14]  Eyal Dassau,et al.  Safety Constraints in an Artificial Pancreatic β Cell: An Implementation of Model Predictive Control with Insulin on Board , 2009, Journal of diabetes science and technology.

[15]  Dale E. Seborg,et al.  Control-Relevant Models for Glucose Control Using A Priori Patient Characteristics , 2012, IEEE Transactions on Biomedical Engineering.

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

[17]  G. Steil,et al.  Feasibility of Automating Insulin Delivery for the Treatment of Type 1 Diabetes , 2006, Diabetes.

[18]  Lauren M. Huyett,et al.  Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms , 2014, Diabetes Care.

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