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References    325




                  [43] Taleb N, Haidar A, Messier V, Gingras V, Legault L, Rabasa-Lhoret R. Glucagon in arti-
                      ficial pancreas systems: potential benefits and safety profile of future chronic use. Dia-
                      betes, Obesity and Metabolism 2017;19(1):13e23.
                  [44] Russell SJ, et al. Outpatient glycemic control with a bionic pancreas in type 1 diabetes.
                      The New England Journal of Medicine 2014;371(4):313e25.
                  [45] Haidar A, Legault L, Messier V, Mitre TM, Leroux C, Rabasa-Lhoret R. Comparison of
                      dual-hormone artificial pancreas, single-hormone artificial pancreas, and conventional
                      insulin pump therapy for glycaemic control in patients with type 1 diabetes: an open-
                      label randomised controlled crossover trial. Lancet Diabetes and Endocrinology
                      2015;3(1):17e26.
                  [46] Ellingsen C, et al. Safety constraints in an artificial pancreatic beta cell: an implemen-
                      tation of model predictive control with insulin on board. Journal of Diabetes Science
                      and Technology 2009;3(3):536e44.
                  [47] Rossetti P, et al. Closed-loop control of postprandial glycemia using an insulin-on-board
                      limitation through continuous action on glucose target. Diabetes Technology and Ther-
                      apeutics 2017;19(6):355e62.
                  [48] Hajizadeh I, et al. Adaptive and personalized plasma insulin concentration estimation
                      for artificial pancreas systems. Journal of Diabetes Science and Technology 2018;
                      12(3):639e49.
                  [49] Steil GM, Rebrin K. Closed loop system for controlling insulin infusion. Google Pat-
                      ents. 2008.
                  [50] Steil GM. Algorithms for a closed-loop artificial pancreas: the case for proportional-
                      integral-derivative control. Journal of Diabetes Science and Technology 2013;7(6):
                      1621e31.
                  [51] Bequette BW. Algorithms for a closed-loop artificial pancreas: the case for model pre-
                      dictive control. Journal of Diabetes Science and Technology 2013;7(6):1632e43.
                  [52] Hovorka R, Canonico V, Chassin LJ, Haueter U, Massi-Benedetti M, Orsini Federici M,
                      Pieber TR, Schaller HC, Schaupp L, Vering. Nonlinear model predictive control of
                      glucose concentration in subjects with type 1 diabetes. Physiological Measurement
                      2005;25(4):905e20.
                  [53] Cinar A, Turksoy K, Hajizadeh I. Multivariable artificial pancreas method and system.
                      2016.
                  [54] Biegler LT, Yang X, Fischer GAG. Advances in sensitivity-based nonlinear model pre-
                      dictive control and dynamic real-time optimization. Journal of Process Control 2015;30:
                      104e16.
                  [55] Yu ZJ, Biegler LT. Advanced-step multistage nonlinear model predictive control. IFAC-
                      PapersOnLine 2018;51(20):122e7.
                  [56] Oberdieck R, Pistikopoulos EN. Explicit hybrid model-predictive control: the exact
                      solution. Automatica 2015;58:152e9.
                  [57] Rivotti P, Pistikopoulos EN. A dynamic programming based approach for explicit
                      model predictive control of hybrid systems. Computers and Chemical Engineering
                      2015;72:126e44.
                  [58] Cao Z, Gondhalekar R, Dassau E, Doyle FJ. Extremum seeking control for personalized
                      zone adaptation in model predictive control for type 1 diabetes. IEEE Transactions on
                      Biomedical Engineering Aug. 2018;65(8):1859e70.
                  [59] Eren-Oruklu M, Cinar A, Rollins DK, Quinn L. Adaptive system identification for esti-
                      mating future glucose concentrations and hypoglycemia alarms. Automatica 2012;
                      48(8):1892e7.
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