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Blood Glucose Regulation in Patients with Type 1 Diabetes by Robust Optimal Safety Critical Control

Navid Moshtaghi Yazdani, Reihaneh Kardehi Moghaddam
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Abstract

Introduction: Diabetes disease is a group of metabolic diseases in which a person has high blood sugar, either because the pancreas does not produce enough insulin, or because cells do not respond to the insulin that is produced. Designing an automated system for regulating blood glucose in patients with diabetes is a solution that researchers have been paying close attention to in recent years. Therefore, safety is the minimum requirement for safety-critical systems such as the artificial pancreas. The present study introduces a safe, robust, performance-guaranteed optimal controller that can safely regulate blood glucose in the disturbance.

Material and Methods: In this section, first, regulate blood glucose levels in simulation studies is evaluated. For this purpose, a dynamic model is used. The model includes a virtual patient, an insulin pump, and a continuous blood glucose level sensor. The virtual patient model represents the dynamics of insulin-glucose, carbohydrate-glucose, and exercise-glucose.

Results: The need to not reset the controller parameters for patients in each category is one of the suggested controller's benefits. However, the PID controller needs to reset the parameters for each group of patients, the predictive control method requires the estimated model of the patient, and its performance is different on different days because the insulin-glucose dynamics for an individual changes day by day.

Conclusion: Taking into account different sensitivities of body tissue to insulin, the results of evaluating the controller for two different groups of patients have shown that the controller is resistant to day-to-day changes in patients who may experience changes in insulin sensitivity, even with stress or medication and will not lose its optimal function. Based on the simulation results, the proposed controller can reduce the external disturbances' effect, whose amplitude is to a good extent within the body's physiological range.

References

Dorner M, Pinget M, Brogard JM. Essential labile diabetes. MMW Munch Med Wochenschr. 1977; 119(19): 671-4. PMID: 406527

How to spot a neglected tropical disease [Internet]. 2019 [cited: 15 Jan 2021]. Available from: http://www.npr.org/blogs/health/2019/06/21/155505445/how-to-spot-a-neglected-tropical-disease

Sarwar N, Gao P, Kondapally Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: A collaborative meta-analysis of 102 prospective studies. Lancet. 2010; 375 (9733): 2215–22. PMID: 20609967 DOI: 10.1016/S0140-6736(10)60484-9

Afrand P, Yazdani N, Moetamedzadeh H, Naderi F, Panahi M. Design and implementation of an expert clinical system for diabetes diagnosis. Global Journal of Science, Engineering and Technology. 2012; 3: 23-31.

Masharani U, German MS. Pancreatic hormones and diabetes mellitus. In: Gardner DG, Shoback D (eds). Greenspan's basic & clinical endocrinology. 9th ed. New York: McGraw-Hill Medical; 2011.

Melmed S, Polonsky K, Larsen PR, Kronenberg H. Williams textbook of endocrinology. 12th ed. Philadelphia: Elsevier/Saunders; 2011.

Cooke DW, Plotnick L. Type 1 diabetes mellitus in pediatrics. Pediatr Rev. 2008; 29(11): 374–84. PMID: 18977856 DOI: 10.1542/pir.29-11-374

Lawrence JM, Contreras R, Chen W, Sacks DA. Trends in the prevalence of preexisting diabetes and gestational diabetes mellitus among a racially/ethnically diverse population of pregnant women, 1999–2005. Diabetes Care. 2008; 31(5): 899–904. PMID: 18223030 DOI: 10.2337/dc07-2345

Doyle FJ, Huyett LM, Lee JB, Zisser HC, Dassau E. Closed-loop artificial pancreas systems: Engineering the algorithms. Diabetes Care. 2014; 37(5): 1191-7. PMID: 24757226 DOI: 10.2337/dc13-2108

Magdelaine N, Chaillous L, Guilhem I, Poirier JY, Krempf M, Moog CH, et al. A long-term model of the glucose-insulin dynamics of type 1 diabetes. IEEE Trans Biomed Eng. 2015; 62(6): 1546-52. PMID: 25615904 DOI: 10.1109/TBME.2015.2394239

Roy A, Parker RS. Dynamic modeling of exercise effects on plasma glucose and insulin levels. J Diabetes Sci Technol. 2007; 1(3): 338-47. PMID: 19885088 DOI: 10.1177/193229680700100305

Kadish A. Automation control of blood sugar: A servomechanism for glucose monitoring and control. Am J Med Electron. 1964; 3: 82-6. PMID: 14150660

Bequette BW. Challenges and recent progress in the development of a closed-loop artificial pancreas. Annu Rev Control. 2012; 36(2): 255-66. PMID: 23175620 DOI: 10.1016/j.arcontrol.2012.09.007

Cobelli C, Renard E, Kovatchev B. Artificial pancreas: Past, present, future. Diabetes. 2011; 60(11): 2672–82. PMID: 22025773 DOI: 10.2337/db11-0654

Dansa M, Pereira Rodrigues VH, Oliveira TR. Blood glucose regulation in patients with type 1 diabetes by means of output-feedback sliding mode control. Control Applications for Biomedical Engineering Systems. 2020; 2020: 25-54.

Batora V, Tarnık M, Murgas J, Schmidt S, Norgaard K, Poulsen N, et al. The contribution of glucagon in an artificial pancreas for people with type 1 diabetes. American Control Conference; 2015.

Reiter M, Reiterer F, del Re L. Bihormonal glucose control using a continuous insulin pump and a glucagon-pen. European Control Conference. IEEE; 2016.

Patti M. New glucagon delivery system reduces episodes of post-bariatric surgery hypoglycemia. Medical XPress, Douglas, Isle of Man; 2018.

Bequette BW, Cameron F, Buckingham BA, Maahs DM, Lum J. Overnight hypoglycemia and hyperglycemia mitigation for individuals with type 1 diabetes: How risks can be reduced. IEEE Control System Magezine. 2018; 38(1): 125–34.

Laxminarayan S, Reifman J, Steil GM. Use of a food and drug administration-approved type 1 diabetes mellitus simulator to evaluate and optimize a proportional-integral-derivative controller. J Diabetes Sci Technol. 2012; 6(6): 1401-12. PMID: 23294787 DOI: 10.1177/193229681200600621

Youssef JE, Castle JR, Branigan DL, Massoud RG, Breen ME, Jacobs PG, et al. A controlled study of the effectiveness of an adaptive closed-loop algorithm to minimize corticosteroid-induced stress hyperglycemia in type 1 diabetes. J Diabetes Sci Technol. 2011; 5(6): 1312-26. PMID: 22226248 DOI: 10.1177/193229681100500602

Makroglou A, Karaoustas I, Li J, Kuang Y. Delay differential equation models in diabetes modeling: A review. Theoretical Biology and Medical Modelling. 2009; 6(1): 1-10.

Lunze K, Singh T, Walter M, Brendel MD, Leonhardt S. Blood glucose control algorithms for type 1 diabetic patients: A methodological review. Biomedical Signal Processing and Control. 2013; 8(2): 107-19.

Lynch SM, Bequette BW. Estimation-based model predictive control of blood glucose intype I diabetics: A simulation study. IEEE Bioengineering Conference Proceedings. IEEE; 2001.

Heydarinejad H, Delavari H. Adaptive fractional order sliding mode controller design for blood glucose regulation-4-3. In: Babiarz A, Czornik A, Klamka J, Niezabitowski M (eds). Theory and applications of non-integer order systems. Springer; 2017.

Abbes IB, Richard PY, Lefebvre MA, Guilhem I, Poirier JY. A closed-loop artificial pancreas using a proportional integral derivative with double phase lead controller based on a new nonlinear model of glucose metabolism. J Diabetes Sci Technol. 2013; 7(3): 699-707. PMID: 23759403 DOI: 10.1177/193229681300700315

Gondhalekar R, Dassau E, Zisser HC, Doyle FJ. Periodic-zone model predictive control for diurnal closed-loop operation of an artificial pancreas. J Diabetes Sci Technol. 2013; 7(6): 1446-60. PMID: 24351171 DOI: 10.1177/193229681300700605

Forlenza GP, Deshpande S, Ly TT, Howsmon DP, Cameron F, Baysal N. Erratum. Application of zone model predictive control artificial pancreas during extended use of infusion set and sensor: A randomized crossover-controlled home-use trial. Diabetes Care. 2017; 40(11): 1606. PMID: 28887408 DOI: 10.2337/dc17-er11a

Leon BS, Alanis AA, Sanchez EN, Ornelas-Tellez F, Ruiz-Velazquez E. Inverse optimal neural control of blood glucose level for type 1 diabetes mellitus patients. Journal of the Franklin Institute. 2012; 349(5): 1851-70.

Hosseini H, Khatibi Bardsiri A. Improving diagnosis accuracy of diabetic disease using radial basis function network and fuzzy clustering. Front Health Inform. 2019; 8(1): e24.

Ebrahimi M, Ahmadi K. Diabetes-related complications severity analysis based on hybrid fuzzy multi-criteria decision making approaches. Front Health Inform. 2017; 6(1): 11–22.

Ahmad I, Munir F, Munir MF. An adaptive backstepping based non-linear controller for artificial pancreas in type 1 diabetes patients. Biomedical Signal Processing and Control. 2019; 47: 49-56.

Kovatchev BP, Breton M, Dalla Man C, Cobelli C. In silico preclinical trials: A proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol. 2009; 3(1): 44–55. PMID: 19444330 DOI: 10.1177/193229680900300106

Leon BS, Alanis AY, Sanchez EN, Ornelas-Tellez F, Ruiz Velazquez E. Subcutaneous neural inverse optimal control for an artificial pancreas. International Joint Conference on Neural Networks. IEEE; 2013.

Facchinetti GSA, Cobelli C. Modeling the error of continuous glucose monitoring sensor data: Critical aspects discussed through simulation studies. J Diabetes Sci Technol. 2010; 4(1): 4-14. PMID: 20167162 DOI: 10.1177/193229681000400102

Isidori A, Astolfi A. Disturbance attenuation and H∞-control via measurement feedback in nonlinear systems. IEEE Transactions on Automatic Control. 1992; 37(9): 1283–93.

Kolathaya S, Ames AD. Input-to-state safety with control barrier functions. IEEE Control Systems Letters. 2018; 3(1): 108–13.

Khaloozadeh H. Optimal blood glucose insulin control of type 1 diabetic patient based on nonlinear delayed models. Control Journal. 2014; 8(4): 31-41.

Camacho EF, Alba CB. Model predictive control. Springer Science & Business Media; 2013.

Utkin VI. Sliding mode control design principles and applications to electric drives. IEEE Transactions on Industrial Electronics. 1993; 40(1): 23-36.




DOI: http://dx.doi.org/10.30699/fhi.v10i1.286

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