Artificial pancreas (AP) systems have shown to improve glucose regulation in type 1 diabetes (T1D) patients. However, full closed-loop performance remains a challenge particularly in children and adolescents, since these age groups often present the worst glycemic control. In this work, an algorithm based on switched control and time-varying IOB constraints is presented. The proposed control strategy is evaluated in silico using the FDA-approved UVA/ Padova simulator and its performance contrasted with the previously introduced Automatic Regulation of Glucose (ARG) algorithm in the pediatric population. The effect of unannounced meals is also explored. Results indicate that the proposed strategy achieves lower hypo- and hyperglycemia than the ARG for both announced and unannounced meals.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Haidar A (2016) The artificial pancreas: how closed-loop control is revolutionizing diabetes. IEEE Control Syst 36(5):28–47
Bequette B (2012) Challenges and recent progress in the development of a closed-loop artificial pancreas. Annu Rev Control 36:255–266
Shi D, Dassau E, Doyle FJ (2019) Adaptive zone model predictive control of artificial pancreas based on glucose- and velocity-dependent control penalties. IEEE Trans Biomed Eng 66(4):1045–1054
Abitbol A, et al (2018) Overnight glucose control with dual- and single-hormone artificial pancreas in type 1 diabetes with hypoglycemia unawareness: a randomized controlled trial. Diabetes Technol Ther 20 (3):189–196
Bally L, et al. (2017) Day-and-night glycaemic control with closed-loop insulin delivery versus conventional insulin pump therapy in free-living adults with well controlled type 1 diabetes: an open-label, randomised, crossover study. Lancet Diabetes Endocrinol 5(4):261–270
Steil G (2013) Algorithms for a closed-loop artificial pancreas: the case for proportional-integral-derivative control. J Diabetes Sci Technol 7(6):1621–1631
Ly T, et al (2015) Day and night closed-loop control using the integrated Medtronic hybrid closed-loop system in type 1 diabetes at diabetes camp. Diabetes Care 38(7):1205–1211
Beneyto A, Vehi J (2018) Postprandial fuzzy adaptive strategy for a hybrid proportional derivative controller for the artificial pancreas. Med Biolog Eng Comput 56(11):1973–1986
Mauseth R, Hirsch IB, Bollyky J, Kircher R, Matheson D, Sanda S, Greenbaum C (2013) Use of a “fuzzy logic” controller in a closed-loop artificial pancreas. Diabetes Technol Ther 15(8):628–633
Sánchez-Peña RS, Cherñavvsky DR (2019) The artificial pancreas: current situation and future directions. Academic Press, London
Brazeau AS, et al (2013) Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes. Diabetes Res Clin Pract 99(1):19–23
Sánchez-Peña R, et al (2018) Artificial pancreas: clinical study in Latin America without premeal insulin boluses. J Diabetes Sci Technol 12(5):914–925
Colmegna P, et al (2018) Automatic regulatory control in type 1 diabetes without carbohydrate counting. Control Eng Pract 22–32
Revert A, et al (2013) Safety auxiliary feedback element for the artificial pancreas in type 1 diabetes. IEEE Trans Biomed Eng 60(8):2113–2122
Powers SW, et al (2002) Parent report of mealtime behavior and parenting stress in young children with type 1 diabetes and in healthy control subjects. Diabetes Care 25(2):313
Turksoy K, et al (2017) Real-time insulin bolusing for unannounced meals with artificial pancreas. Control Eng Pract 59:159–164
Cameron FM, et al (2017) Closed-loop control without meal announcement in type 1 diabetes. Diabetes technol Ther 19(9):527–532
Blauw H, et al (2016) Performance and safety of an integrated bihormonal artificial pancreas for fully automated glucose control at home. Diabetes, Obesity Metabolism 18(7):671–677
Fushimi E, et al (2019) Artificial pancreas: evaluating the arg algorithm without meal announcement. J Diabetes Sci Technol 1035–1043
Fushimi E, Rosales N, De Battista H, Garelli F (2018) Artificial pancreas clinical trials: moving towards closed-loop control using insulin-on-board constraints. Biomed Signal Process Control 45:1–9
Colmegna P, Sánchez-Peña R, Gondhalekar R (2018) Linear parameter-varying model to design control laws for an artificial pancreas. Biomed Signal Process Control 40:204–213
Dalla Man C, Rizza R, Cobelli C (2007) Meal simulation model of the glucose-insulin system. IEEE Trans Biomed Eng 54(10):1740–1749
Dalla Man C, et al (2014) The UVA/Padova type 1 diabetes simulator: new features. J Diabetes Sci Technol 8(1):26–34
León-Vargas F, et al (2015) Postprandial response improvement via safety layer in closed-loop blood glucose controllers. Biomed Signal Process Control 16:80–87
DeJournett L (2010) Essential elements of the native glucoregulatory system, which, if appreciated, may help improve the function of glucose controllers in the intensive care unit setting. J Diabetes Sci Technol 4 (1):190–198
Goodwin GC, et al (2015) A fundamental control limitation for linear positive systems with application to type 1 diabetes treatment. Automatica 55:73–77
Kovatchev B (2019) Glycemic variability: Risk factors, assessment, and control. J Diabetes Sci Technol 13(4):627–635
Battelino T, et al (2019) Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the international consensus on time in range, Diabetes Care, p dci190028
Research in this area is supported by the Argentine government (PICT 2017-3211 Agencia Nacional de Promoción Científica y Tecnológica, PIP 112-201501-00837 CONICET, and UNLP 11/I216).
Conflict of interest
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Here, in detail, tables are shown in order to provide a more accurate representation of the results from the in silico study performed in this paper. All tables show median [Q1–Q3]. Statistical significance is shown in superscripts (∗ for p value < 0.05, ⋆ for p value < 0.01, † for p value < 0.005).
Rights and permissions
About this article
Cite this article
Fushimi, E., Serafini, M.C., De Battista, H. et al. Automatic glycemic regulation for the pediatric population based on switched control and time-varying IOB constraints: an in silico study. Med Biol Eng Comput 58, 2325–2337 (2020). https://doi.org/10.1007/s11517-020-02213-w
- Artificial pancreas
- Switched control
- Insulin on board
- Constrained control