Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties

<italic>Objective:</italic> Zone model predictive control (MPC) has been proven to be an efficient approach to closed-loop insulin delivery in clinical studies. In this paper, we aim to safely reduce mean glucose levels by proposing control penalty adaptation in the cost function of zone MPC. <italic>Methods:</italic> A zone MPC method with a dynamic cost function that updates its control penalty parameters in real time according to the predicted glucose and its rate of change is developed. The proposed method is evaluated on the entire 100-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the zone MPC tested in an extended outpatient study. <italic>Results:</italic> For unannounced meals, the proposed method leads to statistically significant improvements in terms of mean glucose (153.8 mg/dL vs. 159.0 mg/dL; <inline-formula><tex-math notation="LaTeX">$\boldsymbol p<0.001$</tex-math></inline-formula>) and percentage time in <inline-formula><tex-math notation="LaTeX">$[70, 180]$</tex-math></inline-formula> mg/dL (<inline-formula><tex-math notation="LaTeX">$\text{70.5}\%$</tex-math></inline-formula> vs. <inline-formula><tex-math notation="LaTeX">$\text{66.3}\%$</tex-math></inline-formula>; <inline-formula><tex-math notation="LaTeX">$\boldsymbol p<0.001$</tex-math></inline-formula>) without increasing the risk of hypoglycemia. Performance for announced meals is similar to that obtained without adaptation. The proposed method also behaves properly and safely for scenarios of moderate meal-bolus and basal rate mismatches, as well as simulated unannounced exercise. Advisory-mode analysis based on clinical data indicates that the method can reduce glucose levels through suggesting additional safe amounts of insulin on top of those suggested by the zone MPC used in the study. <italic>Conclusion:</italic> The proposed method leads to improved glucose control without increasing hypoglycemia risks. <italic>Significance:</italic> The results validate the feasibility of improving glucose regulation through glucose- and velocity-dependent control penalty adaptation in MPC design.

[1]  R. Hovorka,et al.  Coming of age: the artificial pancreas for type 1 diabetes , 2016, Diabetologia.

[2]  Claudio Cobelli,et al.  Toward a Run-to-Run Adaptive Artificial Pancreas: In Silico Results , 2018, IEEE Transactions on Biomedical Engineering.

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

[4]  Dawei Shi,et al.  Zone Model Predictive Control with Glucose- and Velocity-Dependent Control Penalty Adaptation for an Artificial Pancreas , 2018, 2018 Annual American Control Conference (ACC).

[5]  Eyal Dassau,et al.  Intraperitoneal insulin delivery provides superior glycaemic regulation to subcutaneous insulin delivery in model predictive control‐based fully‐automated artificial pancreas in patients with type 1 diabetes: a pilot study , 2017, Diabetes, obesity & metabolism.

[6]  Eyal Dassau,et al.  Closed-Loop Control of Artificial Pancreatic $\beta$ -Cell in Type 1 Diabetes Mellitus Using Model Predictive Iterative Learning Control , 2010, IEEE Transactions on Biomedical Engineering.

[7]  Irl B. Hirsch,et al.  Multivariable adaptive identification and control for artificial pancreas systems , 2015 .

[8]  Eyal Dassau,et al.  Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial. , 2015, The Journal of clinical endocrinology and metabolism.

[9]  B. Buckingham,et al.  Closed-loop control in type 1 diabetes. , 2016, The lancet. Diabetes & endocrinology.

[10]  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.

[11]  Eyal Dassau,et al.  Outpatient Closed-Loop Control with Unannounced Moderate Exercise in Adolescents Using Zone Model Predictive Control. , 2017, Diabetes technology & therapeutics.

[12]  Eyal Dassau,et al.  An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions , 2017, Journal of diabetes science and technology.

[13]  Robert A. Vigersky,et al.  Glucose concentrations of less than 3.0 mmol/l (54 mg/dl) should be reported in clinical trials: a joint position statement of the American Diabetes Association and the Europian Association for the Study of Diabetes , 2016, Diabetologia.

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

[15]  Eyal Dassau,et al.  Switched LPV Glucose Control in Type 1 Diabetes , 2016, IEEE Transactions on Biomedical Engineering.

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

[17]  Howard C. Zisser,et al.  Outcome Measures for Artificial Pancreas Clinical Trials: A Consensus Report , 2016, Diabetes Care.

[18]  Francis J. Doyle,et al.  A run‐to‐run framework for prandial insulin dosing: handling real‐life uncertainty , 2007 .

[19]  Howard Zisser,et al.  Glucose Estimation and Prediction through Meal Responses Using Ambulatory Subject Data for Advisory Mode Model Predictive Control , 2007, Journal of diabetes science and technology.

[20]  Ali Cinar,et al.  Adaptive control of artificial pancreas systems - a review. , 2014, Journal of healthcare engineering.

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

[22]  C. C. Palerm,et al.  A Run-to-Run Control Strategy to Adjust Basal Insulin Infusion Rates in Type 1 Diabetes. , 2008, Journal of process control.

[23]  B. Wayne Bequette,et al.  Extended multiple model prediction with application to blood glucose regulation , 2012 .

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

[25]  Eyal Dassau,et al.  Periodic zone-MPC with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat type 1 diabetes , 2016, Autom..

[26]  Eyal Dassau,et al.  Dynamic Insulin on Board: Incorporation of Circadian Insulin Sensitivity Variation , 2013, Journal of diabetes science and technology.

[27]  Eyal Dassau,et al.  Twelve-Week 24/7 Ambulatory Artificial Pancreas With Weekly Adaptation of Insulin Delivery Settings: Effect on Hemoglobin A1c and Hypoglycemia , 2017, Diabetes Care.

[28]  C. Cobelli,et al.  The UVA/PADOVA Type 1 Diabetes Simulator , 2014, Journal of diabetes science and technology.

[29]  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.

[30]  Eyal Dassau,et al.  Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial , 2017, Diabetes Care.

[31]  Eyal Dassau,et al.  Multi-Zone-MPC: Clinical Inspired Control Algorithm for the Artificial Pancreas , 2011 .

[32]  Eyal Dassau,et al.  Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems , 2018, IEEE Transactions on Biomedical Engineering.

[33]  Eyal Dassau,et al.  Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control. , 2016, Industrial & engineering chemistry research.

[34]  Eyal Dassau,et al.  Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance , 2018, Autom..

[35]  Stephen P. Boyd,et al.  Dynamic energy management with scenario-based robust MPC , 2017, 2017 American Control Conference (ACC).

[36]  Glucose Concentrations of Less Than 3.0 mmol/L (54 mg/dL) Should Be Reported in Clinical Trials: A Joint Position Statement of the American Diabetes Association and the European Association for the Study of Diabetes , 2016, Diabetes Care.