Volume 16, Issue 2 (Journal of Control, V.16, N.2 Summer 2022)                   JoC 2022, 16(2): 25-39 | Back to browse issues page

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Kouchaki Z, Yousefi M R, Shojaei K. Robust Adaptive Neural Control of the Blood Glucose for Type 1 Diabetic Patients in Presence of Meals. JoC 2022; 16 (2) :25-39
URL: http://joc.kntu.ac.ir/article-1-789-en.html
1- Najafabad Branch, Islamic Azad University
Abstract:   (3324 Views)
In this paper, the blood glucose control for type 1 diabetic patients in the presence of model uncertainties and uncertain meals is considered. In order to present an efficient control approach, it is assumed that the dynamics describe the mechanism of the blood glucose regulation in type 1 diabetic patients are completely unknown. Hence, based on the universal approximation property of the radial basis neural network equipped with an adaptive algorithm, as well as using the minimal learning parameter approach, unknown model dynamics are approximated. Then, based on the feedback-linearization approach and robust adaptive compensation scheme, an appropriate control method is designed to regulate blood glucose in type 1 diabetic patients in the presence of a meal. By a Lyapunov based analysis, it is shown that the closed-loop system of blood glucose control is uniformly ultimately bounded and that the blood glucose of diabetic patients converges to a desired level. Finally, simulation results are shown to demonstrate the efficiency of the designed controller.
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Type of Article: Research paper | Subject: Special
Received: 2020/09/2 | Accepted: 2021/04/22 | ePublished ahead of print: 2021/06/30

References
1. [1] T. Daisuke, Y. Xiao, F. Hu, F. Lewis. "A survey of insulin-dependent diabetes-part I: therapies and devices." International journal of telemedicine and applications, vol. 43, no. 11, pp. 1617-1632, 2008.
2. [2] A. Ciancio, R. Bosio, S. Bo, M. Pellegrini, M. Sacco. "Significant improvement of glycemic control in diabetic patients with HCV infection responding to direct‐acting antiviral agents." Journal of medical virology, vol. 90, no. 2, pp, 320-327, 2018. [DOI:10.1002/jmv.24954]
3. [3] N. Revital, E. Dassau, T. Segall. "Adjusting insulin doses in patients with type 1 diabetes who use insulin pump and continuous glucose monitoring: Variations among countries and physicians." Diabetes, obesity and metabolism, vol. 20, no. 10, pp. 2458-2466, 2018. [DOI:10.1111/dom.13408]
4. [4] A. Roy, R. S. Parker. "Dynamic modeling of exercise effects on plasma glucose and insulin levels." IFAC proceedings volumes, vol. 39, no. 2, pp. 509-514, 2006. [DOI:10.3182/20060402-4-BR-2902.00509]
5. [5] A. Abu-Rmileh, G. Winston, D. Zambrano. "Internal model sliding mode control approach for glucose regulation in type 1 diabetes". Biomedical signal processing and control, vol. 5, no. 2, pp. 94-102, 2011. [DOI:10.1016/j.bspc.2009.12.003]
6. [6] A. Abu-Rmileh, W. Garcia-Gabin, D. Zambrano. "A robust sliding mode controller with internal model for closed-loop artificial pancreas". Medical & biological engineering & computing. vol. 48, no. 12, pp. 1191-201, 2011. [DOI:10.1007/s11517-010-0665-3]
7. [7] A. Nath, R. Dey. "Robust observer based control for plasma glucose regulation in type 1 diabetes patient using attractive ellipsoid method". IET systems biology, vol. 13, no. 2, pp. 84-91, 2019. [DOI:10.1049/iet-syb.2018.5054]
8. [8] S. Ahmad, N. Ahmed, M. Iyas, W. Khan. "Supertwisting sliding mode control algorithm for developing artificial pancreas in type 1 diabetes patients". Biomedical signal processing and control, vol .38, pp. 200-211, 2017. [DOI:10.1016/j.bspc.2017.06.009]
9. [9] S. T. Dinani, M. Zekri, M. Kamali. "Regulation of blood glucose concentration in type 1 diabetics using single order sliding mode control combined with fuzzy on-line tunable gain, a simulation study". Journal of medical signals and sensors, vol. 5, no. 3, pp.131-140, 2015. [DOI:10.4103/2228-7477.161463]
10. [10]R. Zamani Forooshani, M. Siahi, A. Ramezani. "Adaptive type-2 fuzzy control for regulation of glucose level in type 1 diabetes". IETE journal of research, pp. 1-11, 2019. [DOI:10.1080/03772063.2019.1595183]
11. [11] E-O. Meriyan, A. Cinar, L. Quinn, D. Smith. "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. [DOI:10.1016/j.jprocont.2009.04.004]
12. [12] Sh. Hassan, A. Raja. "Closed loop blood glucose control in diabetics." Biomedical research, vol. 28, no. 16, pp. 7230-7236, 2017.
13. [13] S. Deirdre, R. K. Munje. "Backstepping SMC for blood glucose control of type-1 diabetes mellitus patients". International journal of engineering technology science and research, vol. 4, no. 5, pp. 1-7 2017.
14. [14] H. Heydarinejad, H. Delavari. "Fractional order backstepping sliding mode control for blood glucose regulation in type I diabetes patients". Neurocomputing, vol. 12, pp. 187-202, 2017. [DOI:10.1007/978-3-319-45474-0_18]
15. [15] H. Heydarinejad, H. Delavari, D. Baleanu. "Fuzzy type-2 fractional backstepping blood glucose control based on sliding mode observer." International journal of dynamics and control, vol. 7, no. 1, pp. 341-354, 2019. [DOI:10.1007/s40435-018-0445-8]
16. [16] Chen Z, Huang F, Yang C, Yao B. "Adaptive fuzzy backstepping control for stable nonlinear bilateral teleoperation manipulators with enhanced transparency performance". IEEE transactions on industrial electronics. vol. 67, pp. 746-756, 2019. [DOI:10.1109/TIE.2019.2898587]
17. [17] Wang, Min, Shuzhi Sam Ge, and Keum-Shik Hong. "Approximation-based adaptive tracking control of pure-feedback nonlinear systems with multiple unknown time-varying delays." IEEE transactions on neural networks, vol. 21, no. 11, pp. 1804-1816, 2010. [DOI:10.1109/TNN.2010.2073719]
18. [18] Zhou, Qi, Peng Shi, Honghai Liu, and Shengyuan Xu. "Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems." IEEE transactions on systems, man, and cybernetics, part B (cybernetics), vol. 42, no. 6, pp. 1608-1619, 2012. [DOI:10.1109/TSMCB.2012.2196432]
19. [19] Zhou, Qi, Peng Shi, Shengyuan Xu, and Hongyi Li. "Observer-based adaptive neural network control for nonlinear stochastic systems with time delay." IEEE transactions on neural networks and learning systems, vol. 24, no. 1, pp. 71-80, 2012. [DOI:10.1109/TNNLS.2012.2223824]
20. [20] Khalil HK, Grizzle JW. Nonlinear systems. Upper Saddle River, NJ: Prentice hall; 2002 Jan.
21. [21] Kim YH, Lewis FL. "Neural network output feedback control of robot manipulators". IEEE transactions on robotics and automation. vol. 15, pp. 301-309, 1999. [DOI:10.1109/70.760351]
22. [22] G. Tao, PV. Kokotovic. "Adaptive control of systems with actuator and sensor nonlinearities". New York, John Wiley & Sons, 1996.

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