Robust adaptive observer-based control of blood glucose level for type 1 diabetic patient

• Autorzy: Seyedabadi Masoud , Akbarzadeh Kalat Ali

Identyfikatory

DOI

10.1016/j.bbe.2024.03.003

• Języki publikacji: EN

Abstrakty

EN

In this paper, an adaptive controller is designed to regulate the blood glucose level of type 1 diabetes mellitus while not all states of the system are measurable and also its parameters are unknown. The main goal in the control of diabetes is to preserve blood glucose level within a safe rang by a suitable injecting insulin rate to the patient. Herein, it is achieved by measuring the blood glucose level and proposed an observer based adaptive control system. In the proposed method, firstly, the dynamic equations of nonlinear Bergman minimal model (BMM) are transformed into a companion form. Then an adaptive observer is presented to simultaneously estimate the state variables and the system’s parameters. Afterward, based on the designed observer and using a new meal simulation model, an adaptive control is presented to bring back the blood glucose level to its safe range. The overall stability of the developed adaptive control is established using the Lyapunov direct method. Simulation results have been performed to verify the effectiveness of the proposed approach in tracking the desired blood glucose.

Słowa kluczowe

EN

adaptive control; adaptive state observer; type 1 diabetes; T1DM; Bergman minimal model; BMM; Lyapunov stability theory

PL

sterowanie adaptacyjne; obserwator stanu; cukrzyca typu 1; T1DM; BMM; teoria stabilności Lapunova

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 2
• Strony: 295--303
• Opis fizyczny: Bibliogr. 72 poz., rys., tab., wykr.

Twórcy

autor

Seyedabadi Masoud

• Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

autor

Akbarzadeh Kalat Ali

• Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

Bibliografia

• [1] Cobelli C, Dalla Man C, Sparacino G, Magni L, De Nicolao G, Kovatchev BP. Diabetes: models, signals, and control. IEEE Rev Biomed Eng 2009;2:54-96.
• [2] Kumari SK, Mathana JM. Blood sugar level indication through chewing and swallowing from acoustic MEMS sensor and deep learning algorithm for diabetic management. J Med Syst 2019;43:1-9.
• [3] World Health Organization. “Classification of diabetes mellitus.” 2019.
• [4] Tönnies T, Rathmann W, Hoyer A, Brinks R, Kuss O. Quantifying the underestimation of projected global diabetes prevalence by the international diabetes federation (IDF) diabetes atlas. BMJ Open Diabetes Res Care 2021;9(1): e002122.
• [5] Messori M, Toffanin C, Del Favero S, De Nicolao G, Cobelli C, Magni L. Model individualization for artificial pancreas. Comput Methods Programs Biomed 2019; 171:133-40.
• [6] Haidar A. The artificial pancreas: how closed-loop control is revolutionizing diabetes. IEEE Control Syst Mag 2016;36(5):28-47.
• [7] Bekiari E, Kitsios K, Thabit H, Tauschmann M, Athanasiadou E, Karagiannis T, et al. Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis. BMJ 2018:361.
• [8] Quiroz G. The evolution of control algorithms in artificial pancreas: a historical perspective. Annu Rev Control 2019;1(48):222-32.
• [9] MohammadRidha T, Aït-Ahmed M, Chaillous L, Krempf M, Guilhem I, Poirier JY, et al. Model free iPID control for glycemia regulation of type-1 diabetes. IEEE Trans Biomed Eng 2017;65(1):199-206.
• [10] Palerm CC. Physiologic insulin delivery with insulin feedback: a control systems perspective. Comput Methods Programs Biomed 2011;102(2):130-7.
• [11] Soylu S, Danisman K. In silico testing of optimized fuzzy P+D controller for artificial pancreas. Biocybernetics and Biomedical Engineering 2018;38(2): 399-408.
• [12] Hovorka R, Canonico V, Chassin LJ, Haueter U, Massi-Benedetti M, Federici MO, et al. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol Meas 2004;25(4):905.
• [13] Abu-Rmileh A, Garcia-Gabin W. A gain-scheduling model predictive controller for blood glucose control in type 1 diabetes. IEEE Trans Biomed Eng 2009;57(10): 2478-84.
• [14] Wang Y, Dassau E, Doyle FJ. Closed-loop control of artificial pancreatic $\beta $-cell in type 1 diabetes mellitus using model predictive iterative learning control. IEEE Trans Biomed Eng 2009;57(2):211-9.
• [15] Boiroux D, Duun-Henriksen AK, Schmidt S, Nørgaard K, Poulsen NK, Madsen H, et al. Adaptive control in an artificial pancreas for people with type 1 diabetes. Control Eng Pract 2017;58:332-42.
• [16] Batmani Y, Khodakaramzadeh S, Moradi P. Automatic artificial pancreas systems using an intelligent multiple-model PID strategy. IEEE J Biomed Health Inform 2021;26(4):1708-17.
• [17] Ruiz-Velázquez E, Femat R, Campos-Delgado DU. Blood glucose control for type I diabetes mellitus: a robust tracking H∞ problem. Control Eng Pract 2004;12(9): 1179-95.
• [18] Chee F, Savkin AV, Fernando TL, Nahavandi S. Optimal H/sup/spl infin//insulin injection control for blood glucose regulation in diabetic patients. IEEE Trans Biomed Eng 2005;52(10):1625-31.
• [19] Fushimi E, De Battista H, Garelli F. A dual-hormone multicontroller for artificial pancreas systems. IEEE J Biomed Health Inform 2022;26(9):4743-50.
• [20] Nandi S, Singh T. Glycemic control of people with type 1 diabetes based on probabilistic constraints. IEEE J Biomed Health Inform 2018;23(4):1773-83.
• [21] Mohammadzadeh A, Kumbasar T. A new fractional-order general type-2 fuzzy predictive control system and its application for glucose level regulation. Appl Soft Comput 2020;91:106241.
• [22] Kaveh P, Shtessel YB. Blood glucose regulation using higher-order sliding mode control. International Journal of Robust and Nonlinear Control: IFAC-Affiliated Journal 2008;18(4-5):557-69.
• [23] Garcia-Gabin W, Vehí J, Bondia J, Tarín C, Calm R. Robust sliding mode closed-loop glucose control with meal compensation in type 1 diabetes mellitus. IFAC Proceedings Volumes 2008;41(2):4240-5.
• [24] Turksoy K, Cinar A. Adaptive control of artificial pancreas systems-a review. Journal of healthcare engineering 2014;5:1-22.
• [25] Eren-Oruklu M, Cinar A, Quinn L, Smith D. Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes. J Process Control 2009;19 (8):1333-46.
• [26] Sepasi S, Kalat AA, Seyedabadi M. An adaptive back-stepping control for blood glucose regulation in type 1 diabetes. Biomed Signal Process Control 2021;66: 102498.
• [27] Ahmad I, Munir F, Munir MF. An adaptive backstepping based non-linear controller for artificial pancreas in type 1 diabetes patients. Biomed Signal Process Control 2019;47:49-56.
• [28] Pan Y, Ji W, Lam HK, Cao L. An improved predefined-time adaptive neural control approach for nonlinear multi-agent systems. IEEE Trans Autom Sci Eng 2023.
• [29] Chen L, Liang H, Pan Y, Li T. Human-in-the-loop consensus tracking control for UAV systems via an improved prescribed performance approach. IEEE Trans Aerosp Electron Syst 2023.
• [30] Bondia J, Romero-Vivo S, Ricarte B, Diez JL. Insulin estimation and prediction: a review of the estimation and prediction of subcutaneous insulin pharmacokinetics in closed-loop glucose control. IEEE Control Syst Mag 2018;38(1):47-66.
• [31] Borri A, Cacace F, De Gaetano A, Germani A, Manes C, Palumbo P, et al. Luenberger-like observers for nonlinear time-delay systems with application to the artificial pancreas: the attainment of good performance. IEEE Control Syst Mag 2017;37(4):33-49.
• [32] Patra AK, Rout PK. Adaptive sliding mode gaussian controller for artificial pancreas in TIDM patient. J Process Control 2017;59:13-27.
• [33] Palumbo P, Pepe P, Panunzi S, De Gaetano A. Time-delay model-based control of the glucose-insulin system, by means of a state observer. Eur J Control 2012;18(6): 591-606.
• [34] Palumbo P, Pepe P, Panunzi S, De Gaetano A. Observer-based closed-loop control for the glucose-insulin system: local input-to-state stability with respect to unknown meal disturbances. In: 2013 American Control Conference; 2013. p. 1751-6.
• [35] Palumbo P, Pepe P, Panunzi S, De Gaetano A. Observer-based glucose control via subcutaneous insulin administration. IFAC Proceedings Volumes 2012;45(18): 107-12.
• [36] Khodakaramzadeh S, Batmani Y, Meskin N. Automatic blood glucose control for type 1 diabetes: a trade-off between postprandial hyperglycemia and hypoglycemia. Biomed Signal Process Control 2019;54:101603.
• [37] Batmani Y, Khodakaramzadeh S. Non-linear estimation and observer-based output feedback control. IET Control Theory Appl 2020;14(17):2548-55.
• [38] Nath A, Deb D, Dey R. Robust observer-based adaptive control of blood glucose in diabetic patients. Int J Control 2021;94(11):3054-67.
• [39] Alam W, Khan Q, Riaz RA, Akmeliawati R, Khan I, Nisar KS. Gain-scheduled observer-based finite-time control algorithm for an automated closed-loop insulin delivery system. IEEE Access 2020;8:103088-99.
• [40] Farahmand B, Dehghani M, Vafamand N, Mirzaee A, Boostani R, Pieper JK. Robust nonlinear control of blood glucose in diabetic patients subject to model uncertainties. ISA Trans 2023;133:353-68.
• [41] Rios-Bolivar, M. Adaptive back-stepping and sliding mode control of uncertain nonlinear systems. PhD diss., University of Sheffield, 1997.
• [42] Li YM, Min X, Tong S. Adaptive fuzzy inverse optimal control for uncertain strict-feedback nonlinear systems. IEEE Trans Fuzzy Syst 2019;28(10):2363-74.
• [43] Tong S, Sun K, Sui S. Observer-based adaptive fuzzy decentralized optimal control design for strict-feedback nonlinear large-scale systems. IEEE Trans Fuzzy Syst 2017;26(2):569-84.
• [44] Steil GM, Clark B, Kanderian S, Rebrin K. Modeling insulin action for development of a closed-loop artificial pancreas. Diabetes Technol Ther 2005;7(1):94-108.
• [45] Sandham WA, Hamilton DJ, Japp A, Patterson K. Neural network and neuro-fuzzy systems for improving diabetes therapy 1998;No. 98CH36286), vol. 3:1438-41.
• [46] Balakrishnan NP, Rangaiah GP, Samavedham L. Review and analysis of blood glucose (BG) models for type 1 diabetic patients. Ind Eng Chem Res 2011;50(21): 12041-66.
• [47] Ali JB, Hamdi T, Fnaiech N, Di Costanzo V, Fnaiech F, Ginoux JM. Continuous blood glucose level prediction of type 1 diabetes based on artificial neural network. Biocybernetics and Biomedical Engineering 2018;38(4):828-40.
• [48] Tresp V, Briegel T, Moody J. Neural-network models for the blood glucose metabolism of a diabetic. IEEE Trans Neural Netw 1999;10(5):1204-13.
• [49] D’Antoni F, Petrosino L, Sgarro F, Pagano A, Vollero L, Piemonte V, et al. Prediction of glucose concentration in children with type 1 diabetes using neural networks: an edge computing application. Bioengineering 2022;9(5):183.
• [50] Zecchin C, Facchinetti A, Sparacino G, Cobelli C. Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information. Comput Methods Programs Biomed 2014;113(1):144-52.
• [51] Alfian G, Syafrudin M, Anshari M, Benes F, Atmaji FT, Fahrurrozi I, et al. Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features. Biocybernetics and Biomedical Engineering 2020;40 (4):1586-99.
• [52] Sparacino G, Zanderigo F, Corazza S, Maran A, Facchinetti A, Cobelli C. Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans Biomed Eng 2007;54(5):931-7.
• [53] Reifman J, Rajaraman S, Gribok A, Ward WK. Predictive monitoring for improved management of glucose levels. J Diabetes Sci Technol 2007 Jul;1(4):478-86.
• [54] Van Herpe T, Espinoza M, Pluymers B, Wouters P, De Smet F, Van den Berghe G, et al. Development of a critically ill patient input-output model. IFAC Proceedings Volumes 2006;39(1):481-6.
• [55] Finan DA, Palerm CC, Doyle FJ, Zisser H, Jovanovic L, Bevier WC, Seborg DE. Identification of empirical dynamic models from type 1 diabetes subject data. In2008 American Control Conference 2008 (pp. 2099-2104). IEEE.
• [56] Bolie VW. Coefficients of normal blood glucose regulation. J Appl Physiol 1961;16 (5):783-8.
• [57] Ackerman E, Gatewood LC, Rosevear JW, Molnar GD. Model studies of bloodglucose regulation. Bull Math Biophys 1965;27:21-37.
• [58] Bergman RN, Ider YZ, Bowden CR, Cobelli C. Quantitative estimation of insulin sensitivity. American Journal of Physiology-Endocrinology And Metabolism 1979; 236(6):E667.
• [59] Bergman RN, Phillips LS, Cobelli C. Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. J Clin Invest 1981;68(6): 1456-67.
• [60] Sturis J, Polonsky KS, Mosekilde E, Van Cauter E. Computer model for mechanisms underlying ultradian oscillations of insulin and glucose. American Journal of Physiology-Endocrinology And Metabolism 1991;260(5):E801-9.
• [61] Tolić IM, Mosekilde E, Sturis J. Modeling the insulin-glucose feedback system: the significance of pulsatile insulin secretion. J Theor Biol 2000;207(3):361-75.
• [62] Li J, Kuang Y, Mason CC. Modeling the glucose-insulin regulatory system and ultradian insulin secretory oscillations with two explicit time delays. J Theor Biol 2006;242(3):722-35.
• [63] Chen CL, Tsai HW, Wong SS. Modeling the physiological glucose-insulin dynamic system on diabetics. J Theor Biol 2010;265(3):314-22.
• [64] Tiran J, Galle KR, Porte D. A simulation model of extracellular glucose distribution in the human body. Ann Biomed Eng 1975;3:34-46.
• [65] Tiran J, Avruch LI, Albisser AM. A circulation and organs model for insulin dynamics. American Journal of Physiology-Endocrinology and Metabolism 1979; 237(4):E331.
• [66] Guyton JR, Foster RO, Soeldner JS, Tan MH, Kahn CB, Koncz L, et al. A model of glucose-insulin homeostasis in man that incorporates the heterogeneous fast pool theory of pancreatic insulin release. Diabetes 1978;27(10):1027-42.
• [67] Sorensen JT. A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes. Massachusetts Institute of Technology; 1985. PhD diss.
• [68] Parker RS, Doyle FJ, Peppas NA. A model-based algorithm for blood glucose control in type I diabetic patients. IEEE Trans Biomed Eng 1999;46(2):148-57.
• [69] Dalla Man C, Camilleri M, Cobelli C. A system model of oral glucose absorption: validation on gold standard data. IEEE Trans Biomed Eng 2006;53(12):2472-8.
• [70] Åström KJ, Wittenmark B. Adaptive control. 2nd ed. Reading, MA: AddisonWesley; 1995.
• [71] Slotine J-J, Li W. Applied nonlinear control, Vol. 199, no. 1. Englewood Cliffs, NJ: Prentice hall; 1991.
• [72] Heydarinejad H, Delavari H, Baleanu D. Fuzzy type-2 fractional backstepping blood glucose control based on sliding mode observer. International Journal of Dynamics and Control 2019;7:341-54.