Abstract
To maintain good health is the biggest challenge of this generation. And the environment we are living is not only polluted but it is hard to find good healthy food everywhere. One of the most dangerous diseases is diabetes; it is incurable and it is a major health problem. A nonlinear model of type 1 diabetes was taken into consideration, which has been implemented in MATLAB-SIMULINK environment. In this research, develo** a system which monitors the glucose level and also regulates the injection of insulin rate for controlling the blood glucose. Mamdani-type fuzzy logic and PID controller is used to stabilize the blood glucose in normal range. The analysis of result is based on disturbances which are incorporated to patient model in the form of meal, delay or noise in glucose sensor. Comparison of Fuzzy controller and PID is done in both situation disturbances and without disturbances. The result of simulation provides the superiority of the proposed Controller.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gyuk P, Vassányi I, Kósa I (2019) Blood glucose level prediction for diabetics based on nutrition and insulin administration logs using personalized mathematical models. J Healthc Eng.https://doi.org/10.1155/2019/8605206
Mohammad TM, Mina R, Kadhum Q, Mahdi S (2011) Back step**-based-PID-controller designed for an artificial pancreas model. Al Khwarizmi Eng J 7(4):54–60
Kovács L (2006) Extension of the Bergman minimal model for the glucose-insulin interaction. Periodica Polytech Electr Eng 50(1–2):23–32
Rungta A, Nadu T (2017) A review on classification of diabetes using fuzzy logic and optimization technique. Int J Comput Intell Res 13(8):2143–2150. http://www.ripublication.com
Eren-Oruklu M, Cinar A, Colmekci C, Camurdan MC (2008) Self-tuning controller for regulation of glucose levels in patients with type 1 diabetes. In Proceedings of the American control conference, July, pp 819–824. https://doi.org/10.1109/ACC.2008.4586594
Pagkalos I, Herrero P, Toumazou C, Georgiou P (2014) Bio-inspired glucose control in diabetes based on an analogue implementation of a β-cell model. IEEE Trans Biomed Circuits Syst 8(2):186–195. https://doi.org/10.1109/TBCAS.2014.2301377
Yadav J, Rani A, Singh V (2016) Performance analysis of fuzzy-PID controller for blood glucose regulation in type-1 diabetic patients. J Med Syst 40(12):1–15. https://doi.org/10.1007/s10916-016-0602-
Gharghory SM, El-Dib DA, Mahmoud M (2016) Low power fuzzy control system for adjusting the blood glucose level. Proc Int Conf Microelectron ICM (December):333–336. https://doi.org/10.1109/ICM.2016.7847883
Gao J, Yi P, Chi Z, Zhu T (2017) A smart medical system for dynamic closed-loop blood glucose-insulin control. Smart Health 1–2(May):18–33. https://doi.org/10.1016/j.smhl.2017.04.001
Id AS, Della RF, Klickstein I, Russell J, Sorrentino F (2019) Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon, pp 1–23
Sivaramakrishnan N (2017) Optimal model based control for blood glucose insulin system using continuous glucose monitoring 9(4):465–469
Ata S, Khan ZH (2017) Model based control of artificial pancreas under meal disturbances. In 2017 international symposium on recent advances in electrical engineering, RAEE 2017, pp 1–6. https://doi.org/10.1109/RAEE.2017.8246033
Bergman RN (2003) The minimal model of glucose regulation: a biography. Adv Exp Med Biol 537:1–19. https://doi.org/10.1007/978-1-4419-9019-8_1
Sánchez ChávesI Y, Morales-Menéndez R, MartÃnez Chapa SO (2005) Linear quadratic control problem in biomedical engineering. Comput Aided Chem Eng 20(C):1195–1200. https://doi.org/10.1016/S1570-7946(05)80041-0
Chee F, Savkin AV, Fernando TL, Nahavandi S (2005) Insulin injection control for blood glucose regulation in diabetic patients 52(10):1625–1631
Kang H, Han K, Choi MY (2012) Mathematical model for glucose regulation in the whole-body system. Islets 4(2):84–93. https://doi.org/10.4161/isl.19505
Faiz-Ul-Hassan, Adil M, Khaqan A, Shuja S, Tiwana MI, Qadeer-ul-Hassan, Malik S, Riaz RA (2017) Closed loop blood glucose control in diabetics. Biomed Res (India) 28(16):7230–7236
Lynch SM, Wayne Bequette B (2002) Model predictive control blood glucose in type 1 diabetics using subcutaneous glucose measurement
Abu-Rmileh A, Garcia-Gabin W (2010) A gain-scheduling model predictive controller for blood glucose control in type 1 diabetes. IEEE Trans Biomed Eng 57(10 PART 1):2478–2484. https://doi.org/10.1109/TBME.2009.2033663
Sasi AYB, Elmalki MA (2013) A fuzzy controller for blood glucose-insulin system. 2013(May), 111–117
Sharma R (2016) Tuning of digital PID controller for blood glucose level of diabetic patient. November 2017, 2–7. https://doi.org/10.1109/RTEICT.2016.7807837
Yasini S, Karimpour A (2008) Active insulin infusion using fuzzy-based closed-loop control
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kumar, V., Singh, A.K. (2022). Design of Fuzzy Controller for Blood Glucose Level. In: Sachdeva, A., Kumar, P., Yadav, O.P., Tyagi, M. (eds) Recent Advances in Operations Management Applications. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-7059-6_9
Download citation
DOI: https://doi.org/10.1007/978-981-16-7059-6_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7058-9
Online ISBN: 978-981-16-7059-6
eBook Packages: EngineeringEngineering (R0)