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طراحی کنترلکننده مبتنی بر استفاده از مشاهدهگر حالت برای تنظیم قند خون بیماران دیابت نوع 1 | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| مقاله 1، دوره 56، شماره 4، 1403، صفحه 467-496 اصل مقاله (1.82 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2024.22825.7683 | ||
| نویسنده | ||
| محمدرضا همایون زاده* | ||
| دانشکده مهندسی، گروه مکانیک، دانشگاه فسا، فسا، ایران | ||
| چکیده | ||
| در این مقاله، یک کنترلکننده غیرخطی مبتنی بر استفاده از مشاهدهگر حالت برای تنظیم غلظت قند خون در بیماران دیابت نوع 1 ارائه شده است. مدل استفاده شده برای بیماری، مدل حداقلی برگمان توسعه یافته است که اختلالات ناشی از وعده غذایی سازگار با هموستاز انسولین-گلوکز در دیابت نوع 1 را در نظر میگیرد. برای طراحی کنترل پسخورد، از روش پسگام استفاده شده است. کنترلکننده پیشنهادی نیازی به اندازهگیری انسولین و غلظت پلاسما ندارد، درحالیکه عملکرد و مقاومت آن در برابر عدم قطعیت بهبود یافته است. در شرایط واقعی، فقط غلظت قند خون قابل اندازهگیری است و غلظت انسولین و سطح پلاسما توسط مشاهدهگر حالت تخمین زده میشوند و این تخمینها به عنوان پسخورد در قانون کنترلی استفاده میشوند. پایداری مجانبی کنترلکننده با استفاده از قضیه لیاپانوف اثبات شده است و همچنین اثبات شده است که سیستم در حضور عدم قطعیت در پارامترهای سیستم و اختلالات خارجی به صورت محدود بودن ورودی-خروجی پایدار است. نتایج تایید میکنند که کنترلکننده پیشنهادی میتواند در حضوراختلالات ناشی از وعده غذایی و نامعینی در پارامترهای سیستم به صورت مجانبی غلظت قند خون را با تزریق مناسب انسولین کنترل کند. | ||
| کلیدواژهها | ||
| پایداری مجانبی؛ تنظیم قند خون؛ روش پسگام؛ طراحی مشاهدهگر؛ قضیه لیاپونوف | ||
| عنوان مقاله [English] | ||
| Controller design based on the use of state observers for blood glucose regulation in patients with type 1 diabetes | ||
| نویسندگان [English] | ||
| Mohamadreza Homayounzade | ||
| Mechanical Engineering department- Fasa University-Fasa-Iran. | ||
| چکیده [English] | ||
| In this paper, an observer-based nonlinear controller for regulating blood glucose concentrations (BGC) in type 1 diabetes mellitus (T1DM) is proposed. The virtual patient model considered is the extended Bergmann minimal model, which is augmented by a meal disturbance and adapted to represent the insulin-glucose homeostasis of T1DM. The backstepping (BS) technique is used to design a closed-loop feedback controller. The proposed controller does not need to measure insulin, and plasma concentrations while improving control performance and robustness against uncertainty. Insulin concentration and plasma levels are estimated using state observers. These estimations are used as feedback to the controller. The asymptotic stability of the observer-based controller is proved using the Lyapunov theorem. Moreover, it is proved that the system is bounded input-bounded output (BIBO) stable in the presence of uncertainties generated by uncertain parameters and external disturbances. For realistic situations, we consider only the BGC to be available for measurement, and additionally, inter-and intra-patient variability of system parameters is considered. The results confirm that the proposed controller can asymptotically regulate BGC by appropriate injection of insulin under meal disturbance and ±%25 of variations in system parameters. | ||
| کلیدواژهها [English] | ||
| Asymptotic Stability, Backstepping Approach, Blood Glucose Concentration, Lyapunov Theorem, Observer Design | ||
| مراجع | ||
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[1] J.W. Maina, Understanding the types and causes of diabetes mellitus, 2018. [2] J.F. Yale, B. Paty, P.A. Senior, Diabetes Canada Clinical Practice Guidelines Expert Committee, Hypoglycemia, Canadian journal of diabetes, 42 (2018) S104-S108. [3] M.G. Tinajero, V.S. Malik, An update on the epidemiology of type 2 diabetes: a global perspective, Endocrinology and Metabolism Clinics, 50(3) (2021) 337-355. [4] A. Cinar, Artificial pancreas systems: An introduction to the special issue, IEEE Control Systems Magazine, 38 (2018) 26-29. [5] A. Bhattacharjee, A. Easwaran, M.K.-S Leow, N. Cho, Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control, Biomedical Signal Processing and Control, 41 (2018) 198-209. [6] A. Haidar, The artificial pancreas: How closed-loop control is revolutionizing diabetes, IEEE Control Systems Magazine, 36 (2016) [7] P. Colmegna, R.S. Sánchez-Peña, R. Gondhalekar, Linear parameter-varying model to design control laws for an artificial pancreas, Biomedical Signal Processing and Control, 40 (2018) 204-213. [8] S.D. Patek, M.D. Breton, Y. Chen, C. Solomon, B. Kovatchev, Linear quadratic gaussian-based closed-loop control of type 1 diabetes, Journal of Diabetes Science and Technology, 1 (2007) 834-841. [9] S. Skogestad, Simple analytic rules for model reduction and PID controller tuning, Journal of process control, 13 (2003) 291-309. [10] G. Marchetti, M. Barolo, L. Jovanovic, H. Zisser, D.E. Seborg, An improved PID switching control strategy for type 1 diabetes, IEEE transactions on biomedical engineering, 55 (2008). [11] A. Nath, R. Dey, V.E. Balas, Closed loop blood glucose regulation of type 1 diabetic patient using Takagi-Sugeno fuzzy logic control, In International Workshop Soft Computing Applications, (2016) 286-296. [12] A.Y.B. Sasi, M.A. Elmalki, A fuzzy controller for blood glucose-insulin system, 2013. [13] M. Elhoushy, B.A. Zalam, A. Sayed, Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach, Biomed. Eng. Lett. 14 (2024) 127-151. [14] A. Sharma, H.P. Nilam, H.P. Singh, Computer-controlled diabetes disease diagnosis technique based on fuzzy inference structure for insulin-dependent patients, Applied Intelligence, 53(2) (2023) 1945-1958. [15] Z. Kochaki, M.R. Yousefi, K. Shojaei, Blood glucose control for type 1 diabetic patients: robust fuzzy adaptive approach, Journal of Intelligent Procedures in Electrical Technology, 14(53) (2023) 171-188. [16] S. Yan, L.L. Chu, Y. Cai, Robust H∞ control of T–S fuzzy blood glucose regulation system via adaptive event-triggered scheme, Biomedical Signal Processing and Control, 83 (2023) 104643. [17] S. Langarica, M. Rodriguez-Fernandez, F. Núñez, F.J. Doyle III, A meta-learning approach to personalized blood glucose prediction in type 1 diabetes, Control Engineering Practice, 135 (2023) 105498. [18] S. Das, A. Nath, R. Dey, S. Chaudhury, Glucose regulation in diabetes patients via insulin pump: A feedback linearisation approach, In Innovations in infrastructure, (2019) 55-65. [19] S.A. Babar, I.A. Rana, M. Arslan, M.W. Zafar, Integral backstepping based automated control of blood glucose in diabetes mellitus type 1 patients, IEEE Access, 7 (2019) 173286-173293. [20] S. Ahmad, N. Ahmed, M. Ilyas, W. Khan, Super twisting sliding mode control algorithm for developing artificial pancreas in type 1 diabetes patients, Biomedical Signal Processing and Control, 38 (2017) 200-211. [21] W. Alam, N. Ali, S. Ahmad, J. Iqbal, Super twisting control algorithm for blood glucose regulation in type 1 diabetes patients, In 15th International Bhurban Conference on Applied Sciences and Technology, (2018) 298-303. [22] N.T. Parsa, A.R. Vali, R. Ghasemi, Back stepping sliding mode control of blood glucose for type I diabetes, World Academy of Science, Engineering and Technology, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 8 (2014) 779-783. [23] A.G.G. Hernández, L. Fridman, A. Levant, Y. Shtessel, R. Leder, C.R. Monsalve, S.I. Andrade, High-order sliding-mode control for blood glucose: Practical relative degree approach, Control Engineering Practice, 21 (2013) 747–758. [24] E. Ruiz-Velázquez, R. Femat, D.U. Campos-Delgado, Blood glucose control for type I diabetes mellitus: A robust tracking H∞ problem, Control engineering practice, 12 (2004) 1179-1195. [25] L. Kovács, Linear parameter varying (LPV) based robust control of type-I diabetes driven for real patient data, Knowledge-Based Systems, 122 (2017) 199-213. [26] H. Khan, J.K. Tar, I. Rudas, L. Kovács, G. Eigner, Receding horizon control of type 1 diabetes mellitus by using nonlinear programming, Complexity, 2018. [27] W. Alam, Q. Khan, R.A. Riaz, R. Akmeliawati, I. Khan, K.S. Nisar, Gain scheduled observer-based finite-time control algorithm for an automated closed-loop insulin delivery system, IEEE Access, 8 (2020)103088-103099. [28] D.K. Parrish, D.B. Ridgely, Control of an artificial human pancreas using the SDRE method, In Proceedings of the American Control Conference, 2 (1997) 1059-1060. [29] C. Li, R. Hu, Fuzzy-PID control for the regulation of blood glucose in diabetes, In WRI Global Congress on Intelligent Systems, 2 (2009) 170-174. [30] M. Homayounzade, Variable structure robust controller design for blood glucose regulation for type 1 diabetic patients: A backstepping approach, IET Systems Biology, 2021. [31] T. Zhou, J.L. Dickson, J. Geoffrey Chase, Autoregressive modeling of drift and random error to characterize a continuous intravascular glucose monitoring sensor, Journal of diabetes science and technology, 12 (2018) 90-104. [32] C.E. De Block, P. Rogiers, P.G. Jorens, T. Schepens, C. Scuffi, L.F. Van Gaal, A comparison of two insulin infusion protocols in the medical intensive care unit by continuous glucose monitoring, Annals of intensive care, 6 (2016) 1-13. [33] T. Wollersheim, L.J. Engelhardt, J. Pachulla, R. Moergeli, S. Koch, C. Spies, ..., S. Weber-Carstens, Accuracy, reliability, feasibility and nurse acceptance of a subcutaneous continuous glucose management system in critically ill patients: a prospective clinical trial, Annals of intensive care, 6 (2016) 1-13. [34] T. Zhou, J.L. Dickson, G.M. Shaw, J.G. Chase, Continuous glucose monitoring measures can be used for glycemic control in the ICU: an in-silico study, Journal of diabetes science and technology, 12 (2018) 7-19. [35] A. Borri, F. Cacace, A. De Gaetano, A. Germani, C. Manes, P. Palumbo, S. Panunzi, P. Pepe, Luenberger-like observers for nonlinear time-delay systems with application to the artificial pancreas: The attainment of good performance, IEEE Control Systems Magazine, 37 (2017a) 33-49. [36] A. Borri, P. Palumbo, C. Manes, S. Panunzi, A. De Gaetano, Sampled-data observer-based glucose control for the artificial pancreas, Acta Polytechnica Hungarica, 14 (2017b) 79-94. [37] A. Nath, D. Deb, R. Dey, Robust observer-based adaptive control of blood glucose in diabetic patients, International Journal of Control, (2020) 1-14. [38] A. Gy¨orgy, L. Kov´acs, P. Szalay, D.A. Drexler, B. Beny´o, Z. Beny´o, Quasi-model-based control of type 1 diabetes mellitus, Journal of Electrical and Computer Engineerin, 4 (2011a). [39] A. Gy¨orgy, L. Kov´acs, P. Szalay, D.A. Drexler, B. Beny´o, Z. Beny´o, Quasi-model-based control of type 1 diabetes mellitus, Journal of Electrical and Computer Engineering, 4 (2011b). [40] W. Liu, F. Tang, Modeling a simplified regulatory system of blood glucose at molecular levels, Journal of Theoretical Biology, 252 (2008) 608-620. [41] M. Mera, A. Polyakov, W. Perruquetti, Finite-time attractive ellipsoid method: implicit lyapunov function approach, International Journal of Control, 89 (2016) 1079-1090. [42] L. Kovács, G. Eigner, M. Siket, L. Barkai, Control of diabetes mellitus by advanced robust control solution, IEEE Access, 7 (2019) 125609-125622. [43] H. Khalil, Nonlinear Systems, Pearson; 3rd edition, 2001. [44] J.J.E. Slotine, W. Li, Applied nonlinear control, Englewood Cliffs, NJ: Prentice hall, 1991. [45] M. Homayounzade, Variable structure robust controller design for blood glucose regulation for type 1 diabetic patients: A backstepping approach, IET Systems Biology, 15(6) (2021) 173-83. [46] G. Pacini, R.N. Bergman, MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test, Computer methods and programs in biomedicine, 23(2) (1986) 113-122. [47] R.N. Bergman, L.S. Phillips, C. Cobelli, Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose, The Journal of clinical investigation, 68(6) (1981) 1456-1467. [48] H. Thabit, R. Hovorka, M. Evans, Artificial pancreas: the bridge to a cure for type 1 diabetes, European Diabetes Nursing, 9(2) (2012) 56-60. [49] K. Rebrin, G.M. Steil, Can interstitial glucose assessment replace blood glucose measurements?, Diabetes technology & therapeutics, 2(3) (2000) 461-472. [50] R. Leproult, E. Van Cauter, Role of sleep and sleep loss in hormonal release and metabolism, Pediatric Neuroendocrinology, 17 (2010) 11-21. | ||
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