نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی پزشکی، دانشکده مهندسی، دانشگاه فردوسی مشهد

2 استادیار گروه مهندسی برق، دانشکده مهندسی، دانشگاه فردوسی مشهد

10.22041/ijbme.2014.13047

چکیده

درحال حاضر، تزریق انسولین در بیماران دیابت نوع یک، اغلب موجب نوسان­های شدید در قند خون آن­ها شده و منجر به رخدادهای هایپرگلیسمی/هایپوگلیسمی می­شود. کنترل حلقه بسته گلوکز توسط پانکراس مصنوعی کیفیت زندگی بیماران دیابت نوع یک را بهبود می­دهد. درین مقاله، با استفاده از سیمولاتور GIM، داده­ی بیمار دیابتی در طول شبانه­روز به دست آمده و سپس به مدل­سازی معکوس رفتار فیزیولوژیکی سیستم پرداخته شد. نظر به ماهیت تأخیری سیستم، در گام بعد، ساختار کنترلی جدیدی برای سیستم­های تأخیر­دار پیشنهاد شده­است که تلفیقی از کنترل مدل مرجعِ تطبیقی و پیش­بین اسمیتِ اصلاح یافته است. با توجه به تنوع پذیری گسترده­ی متابولیسم بیماران مختلف در دنیای واقعی، جمعیتی از 30 بیمار به صورت مجازی و با در نظر گرفتن تغییرات تصادفی و نوسان­های سینوسی در پارامترهای مدل گلوکز/انسولینِ دالمن ایجاد شد تا تغییر ­پذیری بین فردی سیستم تنظیم گلوکز پیاده­سازی شود. عملکرد الگوریتم طراحی شده، براساس شاخص­های کمی و کیفی مورد ارزیابی قرار گرفت و با کنترلر PID در ساختار پیش­بین اسمیت مقایسه شد. نتایج حاکی­از عملکرد مناسب کنترلر پیشنهادی در شرایط ناشتا، دفع اغتشاش غذا و توانایی آن در برابر تغییر پذیری بین بیماران است.

کلیدواژه‌ها

عنوان مقاله [English]

Glucose Regulation in Type 1 Diabetes Mellitus with Model Reference Adaptive Control and Modified Smith Predictor

نویسندگان [English]

  • Zeinab Tashakorizade 1
  • Nadia Naghavi 2
  • Seyed Kamal Hosseini Sani 2

1 M. S. student, Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

2 Assistant Professor, Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad

چکیده [English]

Nature of the system, a novel adaptive control structure has been proposed for time-delayed systems, which is a combination of the model reference adaptive control with modified Smith Predictor. Due to extensive variability among patients in metabolism, an in silico trial consisting of 30 patients with random changes and sinusoidal oscillation in parameters of Dalla Man glucose-insulin model has been used to simulate the personal variability in the glucose control system. Performance of the proposed algorithm has been compared to the PID controller with Smith Predictor, based on the quantitative and qualitative indicatirs. Simulation results show that the proposed control scheme is effective in fasting conditions, meal disturbance rejection, and robustness against inter-patients variability.Insulin therapy for type 1 diabetes patients often causes high fluctuations in their blood glucose and hypoglycemic/hyperglycemic events. Closed loop control of blood glucose using artificial pancreas can improve life quality of patients. In this paper, physiological behaviour of the system has been modeled inversely using daily patient data acquired GIM simulator. Then, considering the delaed.

کلیدواژه‌ها [English]

  • Type 1 diabetes mellitus
  • Model reference adaptive control
  • Time delayed system
  • Smith Predictor
[1]     H. C. Schaller, L. Schaupp, M. Bodenlenz, M. E. Wilinska, L. J. Chassin, P. Wach, T. Vering, R. Hovarka, T. R. Pieber, “On-line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with type 1 diabetes” Diabetic Medicin, Vol 23, No 1, pp 90-93, 2006.
[2]     B. Candas, J. Radziuk, “An adaptive plasma glucose controller based on a nonlinear insulin/glucose model” IEEE Trans. Biomed. Eng. Vol 41, No 2, pp 116-124, 1994.
[3]     D. S. Patek, D. Marc Breton, Y. Chen, C. Solomon, B. Kovatchev, “Linear quadratic Gaussian-based closed-loop control of type 1 diabetes” J Diabetes Sci Technol Vol 1, No 6, pp 834–841, 2007.
[4]     Y. Irma Sánchez Chávez, O. Sergio Martínez Chapa, R. Morales-Menéndez, “Glucose optimal control system in diabetes treatment, Applied Mathematics and Computation” Vol 209, No 1, pp 19-30, 2009.
[5]     M. Eren-Oruklu, A. Cinar, “Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes” Journal of process control Vol 19, No 8, pp 1333-1346, 2009.
[6]     R. Hovorka, V. Canonico, L. J. Chassin, U. Haueter, et al. “Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes” Physiol. Meas Vol 25, No 4, pp 905–920, 2004.
[7]     Y. Wang, F. Doyle, “Closed-loop control of artificial pancreatic ß-cell in type 1 diabetes mellitus using model predictive iterative learning control” IEEE Transactions on Biomedical Engineering Vol 57,  No 2, pp 211-219, 2010.
[8]     K. Lunze, T. Singh, M. Walter, M. Brendel, S. Leonhardt, “Blood glucose control algorithms for type 1 diabetic patients: A methodological review” Biomedical Signal Processing and Control Vol 8, No 2, pp 107– 119, 2013.
[9]     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, Vol 12, No 9, pp 1179-1195, 2004.
[10] F. Chee, A. V. Savkin, T. L. Fernando, S. Nahavandi, “Optimal H insulin injection control for blood glucose regulation in diabetic patients, IEEE Transactions on Biomedical Engineering, Vol 52 , No 10, pp 162-1631, 2005.
[11] J. T. Sorensen, “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.
[12] R. N. Bergman, L .S .Phillips, C. Cobelli, “Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and b-cell glucose sensitivity from the response to intravenous glucose” J. Clin. Invest Vol 68, pp 1456–1467, 1981.
[13] C. Dalla Man, R. Rizza, C. Cobelli, “Meal Simulation Model of the Glucose-Insulin System” IEEE Transaction on biomedical engineering Vol 54, No 10, 2007.
[14] S. Karra, M. N. Karim, B. Han, “Predictive control of blood glucose concentratio in type-I diabetic patients using linear input–output models” In Proc. 10th Int. IFAC Symp. Comp. Appl. Biotech., Cancun, Mexico, Vol 1, pp 147-152, 2007.
[15] X. W. Wong, J. G. Chase, G. M. Shaw, C. E. Hann, T. Lotz, J. Lin, et al. “Model predictive glycemic regulation in critical illness using insulin and nutrition input: a pilot study” Med. Eng. Phys. Vol 28, pp 665–681, 2006.
[16] P. Soru, G. De Nicolao, C. Toffanin, C. Dalla Man, C. obelli, L. Magni,” MPC based Artificial Pancreas: Strategies for individualization and meal compensation” Annual Reviews in Control Vol 36, No 1, pp 118–128, 2012.
[17] G. M. Steil, K. Rebrin, C .Darwin, F. Hariri, M. F. Saad, “Feasibility of automating insulin delivery for the treatment of type 1 diabetes” Diabetes Vol 55, No 12, pp 3344-50, 2006.
[18] L. Magni, D. M. Raimondo, C. DallaMan, G. DeNicolao, B. Kovatchev, C. Cobelli, “Model predictive control of glucose concentration in type 1 diabetes patients” An in silico trialBiomedical Signal processing and control, Vol 4 No 4, pp 338-346, 2009.
[19] D. Finan, C. Palerm, F. Doyle, et al. “Effect of input excitation on the quality of empirical dynamic models for type 1 diabetes” AIChE Journal Vol 55, pp 1135–1146, 2009.
[20] C. Dalla Man, D. M. Raimondo, R. A. Rizza, C. Cobelli, “GIM, Simulation Software of Meal Glucose–Insulin Model” Journal of Diabetes Science and Technolog, Vol 1, No 3, pp 323-330, 2007.
[21] H. Huang, “A modified smith predictor with an approximate inverse of dead time” AiChE Journal Vol 36, No 7, pp 1025-1031, 1990.
[22] C. Palerm, et al. “Prandial insulin dosing using run-to-run control: application of clinical data and medical expertise to define a suitable performance metric” Diabetes Care, Vol.30, No.5, pp 1131-1136, 2007.
[23] B. Kovatchev, E. Otto, D. Cox, L. Gonder-Frederick, W. Clarke, “Evaluation of a new measure of blood glucose variability in diabetes” Diabetes Care Vol 29, No 11, pp 2433–2438, 2006.