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Improved Blood Glucose-Insulin Monitoring with Dual-Layer Predictive Control Design

Authors: Vahid Nademi


In response to widely used wearable medical devices equipped with a continuous glucose monitor (CGM) and insulin pump, the advanced control methods are still demanding to get the full benefit of these devices. Unlike costly clinical trials, implementing effective insulin-glucose control strategies can provide significant contributions to the patients suffering from chronic diseases such as diabetes. This study deals with a key role of two-layer insulin-glucose regulator based on model-predictive-control (MPC) scheme so that the patient’s predicted glucose profile is in compliance with the insulin level injected through insulin pump automatically. It is achieved by iterative optimization algorithm which is called an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) solver for handling uncertainties due to unexpected variations in glucose-insulin values and body’s characteristics. The feasibility evaluation of the discussed control approach is also studied by means of numerical simulations of two case scenarios via measured data. The obtained results are presented to verify the superior and reliable performance of the proposed control scheme with no negative impact on patient safety.

Keywords: Blood glucose monitoring, insulin pump, optimization, predictive control, diabetes disease.

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[1] J. Kropff, S. D. Favero, J. Place, et al., “2 month evening and night closed-loop glucose control in patients with type 1 diabetes under free living conditions: a randomised crossover trial,” Lancet Diabetes Endocrinol., vol. 3, pp. 939-947, Dec. 2015.
[2] M. Vettoretti, A. Facchinetti, G. Sparacino, C. Cobelli, “Type 1 diabetes patient decision simulator for in silico testing safety and effectiveness of insulin treatments”, IEEE Transactions on Biomedical Engineering, vol. 65, no. 6, pp. 1281-1290, June 2018.
[3] E. Johannessen, O. Krushinitskaya, A. Sokolov, et al., “Toward an injectable continuous osmotic glucose sensor,” J. Diab. Sci. Technol., vol. 4, pp. 882-892, Jul. 2010.
[4] S. Schaller, J. Lippert, L. Schaupp, T. R. Pieber, A. Schuppert, and T. Eissing, “Modeling Robust PBPK/PD-Based Model Predictive Control of Blood Glucose”, IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1492-1504, Jul. 2016.
[5] R. Hovorka, “Closed-loop insulin delivery: From bench to clinical practice,” Nature Rev. Endocrinol., vol. 7, no. 7, pp. 385–395, Feb. 2011.
[6] J. R. Castle et al., “Novel use of glucagon in a closed-loop system for prevention of hypoglycemia in type 1 diabetes,” Diabetes Care, vol. 33, no. 6, pp. 1282-1287, June 2010.
[7] A. Dauber et al., “Closed-loop insulin therapy improves glycemic control in children aged <7 years: A randomized controlled trial,” Diabetes Care, vol. 36, no. 2, pp. 222-227, Feb. 2013.
[8] Y. Ruan, M. E. Wilinska, H. Thabit, and R. Hovorka, “Modeling Day-to-Day Variability of Glucose–Insulin Regulation Over 12-Week Home Use of Closed-Loop Insulin Delivery”, IEEE Transactions on Biomedical Engineering, vol. 64, no. 6, pp. 1412-1419, June 2017.
[9] R. Hovorka et al., “Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes,” Physiological Meas., vol. 25, no. 4, pp. 905-920, Aug. 2004.
[10] T. S. Bailey, A. Chang, and M. Christiansen, “Clinical accuracy of a continuous glucose monitoring system with an advanced algorithm”, Journal of Diabetes Sci Technol, vol. 9, no. 2, pp. 209-214, Feb. 2015.
[11] Medtronic Inc. website, available:
[12] H. Park, J. Sun and I. Kolmanovsky, “A tutorial overview of IPA-SQP approach for optimization of constrained nonlinear systems,” in Proc. of 11th World Congress on Intelligent Control and Automation (WCICA), China, Jul. 2014, pp. 1735-1740.