Feedback and feedforward control in the context of model predictive control with application to the management of type 1 diabetes mellitus

Abstract A unified formulation of feedback and feedforward control is given in the context of model predictive control. The ideas are illustrated by the management of type 1 diabetes mellitus although the general principles apply, mutatis mutandis, to other scenarios and problems.

[1]  Graham C. Goodwin,et al.  A fundamental control performance limit for a class of positive nonlinear systems , 2018, Autom..

[2]  Ali Cinar,et al.  Real-time insulin bolusing for unannounced meals with artificial pancreas , 2017 .

[3]  K. Kumareswaran Closed-loop insulin delivery in adults with type 1 diabetes , 2012 .

[4]  Graham C. Goodwin,et al.  Control Limitations in Models of T1DM and the Robustness of Optimal Insulin Delivery , 2018, Journal of diabetes science and technology.

[5]  B. Wayne Bequette,et al.  Challenges and recent progress in the development of a closed-loop artificial pancreas , 2012, Annu. Rev. Control..

[6]  Patricio Colmegna,et al.  Analysis of three T1DM simulation models for evaluating robust closed-loop controllers , 2014, Comput. Methods Programs Biomed..

[7]  Graham C. Goodwin,et al.  A modified relay autotuner for systems having large broadband disturbances , 2018, Autom..

[8]  Robert G. Sutherlin,et al.  A Bihormonal Closed-Loop Artificial Pancreas for Type 1 Diabetes , 2010, Science Translational Medicine.

[9]  C. Cobelli,et al.  Progress in Development of an Artificial Pancreas , 2009, Journal of diabetes science and technology.

[10]  Graham C. Goodwin,et al.  Constrained Control and Estimation: an Optimization Approach , 2004, IEEE Transactions on Automatic Control.

[11]  Gabriele Pannocchia,et al.  Disturbance models for offset‐free model‐predictive control , 2003 .

[12]  Juan I. Yuz,et al.  Sampled-Data Models for Linear and Nonlinear Systems , 2013 .

[13]  Patricio Colmegna,et al.  Linear parameter-varying model to design control laws for an artificial pancreas , 2018, Biomed. Signal Process. Control..

[14]  Patricio Colmegna,et al.  Automatic regulatory control in type 1 diabetes without carbohydrate counting , 2018 .

[15]  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..

[16]  T. Wolever,et al.  Glycemic index of foods: a physiological basis for carbohydrate exchange. , 1981, The American journal of clinical nutrition.

[17]  Benyamin Grosman,et al.  Day and Night Closed-Loop Control Using the Integrated Medtronic Hybrid Closed-Loop System in Type 1 Diabetes at Diabetes Camp , 2015, Diabetes Care.

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

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

[20]  L. Magni,et al.  Multinational Study of Subcutaneous Model-Predictive Closed-Loop Control in Type 1 Diabetes Mellitus: Summary of the Results , 2010, Journal of diabetes science and technology.

[21]  Patrick McElduff,et al.  Extended insulin boluses cannot control postprandial glycemia as well as a standard bolus in children and adults using insulin pump therapy , 2014, BMJ Open Diabetes Research and Care.

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

[23]  Darrell M. Wilson,et al.  A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator , 2009, Journal of diabetes science and technology.

[24]  J. Leahy,et al.  Fully Automated Closed-Loop Insulin Delivery Versus Semiautomated Hybrid Control in Pediatric Patients With Type 1 Diabetes Using an Artificial Pancreas , 2008 .

[25]  W. Tamborlane,et al.  The Artificial Pancreas: Are We There Yet? , 2014, Diabetes Care.

[26]  Scott A. Kaestner,et al.  Microneedle-Based Intradermal Delivery Enables Rapid Lymphatic Uptake and Distribution of Protein Drugs , 2010, Pharmaceutical Research.

[27]  Graham C. Goodwin,et al.  Control System Design , 2000 .

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

[29]  Maciej Henneberg,et al.  Type 1 diabetes prevalence increasing globally and regionally: the role of natural selection and life expectancy at birth , 2016, BMJ Open Diabetes Research and Care.

[30]  C. Cobelli,et al.  The Artificial Pancreas in 2016: A Digital Treatment Ecosystem for Diabetes , 2016, Diabetes Care.

[31]  E. Atlas,et al.  MD-Logic Artificial Pancreas System , 2010, Diabetes Care.

[32]  Daniel Howsmon,et al.  Closed-Loop Control Without Meal Announcement in Type 1 Diabetes. , 2017, Diabetes technology & therapeutics.

[33]  Graham C. Goodwin,et al.  A Critique of Observers Used in the Context of Feedback Control , 2018, ICIRA.

[34]  Carola van Pul,et al.  Model-based analysis of postprandial glycemic response dynamics for different types of food , 2018, Clinical Nutrition Experimental.

[35]  S. Aronoff,et al.  Glucose Metabolism and Regulation: Beyond Insulin and Glucagon , 2004 .

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

[37]  Garry M. Steil,et al.  Identification of Intraday Metabolic Profiles during Closed-Loop Glucose Control in Individuals with Type 1 Diabetes , 2009, Journal of diabetes science and technology.

[38]  Josep Vehí,et al.  A review of personalized blood glucose prediction strategies for T1DM patients , 2017, International journal for numerical methods in biomedical engineering.