Adaptive Integral Backstepping Artificial Pancreas System for Blood Glucose Regulation of Type-1 Diabetic Patients

Isah Usman Yusuf, Muhammed Bashir Mu'azu, Tajudeen Humble sikiru, Zaharadeen Haruna

Abstract


Diabetes Mellitus (DM) is one of the most prevailing ailments nowadays in our society. It is a metabolic disorder caused by an increase in blood glucose concentration (BGC) above the normal range with the disturbance of starchy foods, fat, and protein metabolism thereby leading to the inability of insulin secretion. An adaptive integral backstepping controller for Artificial Pancreas System (APS) is developed to address the issues of hyperglycemia and hypoglycemia episodes which are two major problems of Blood Glucose Concentration (BGC) control for type-1 diabetes mellitus (T1DM) patients as the current method is manual and based on doctor’s advice. Controllers have been designed to automate the regulation process: Proportional Integral Derivative (PID), Fuzzy Logic Controller (FLC), Sliding Mode Controller (SMC), and Integral Backstepping controller (IBC), improved glycemic performance was observed but longer settling of BGC to the basal level. The developed Adaptive Integral Backstepping Control (AIBC) addresses this problem. Firstly, the adaptive parameters of the T1DM patient’s model were developed in Simulink. Then the IBC was designed and applied to the developed model to track the basal threshold. The performance metrics of the developed AIBC system were evaluated against settling time, overshoot, and steady-state error. Simulation of AIBC when patient BG level is 230mg/dl obtained a settling time of 9.73 minutes while both controllers achieved 0% overshoot and 0 steady-state error, a 15.39% settling time improvement compared to IBC. The implication of the developed AIBC is fast steady-state tracking and better settling time, implying quick recovery of patients from hyperglycemia. Also, an impulse signal was applied to the controller (25mg/dl and 45mg/dl with a duration of 4 minutes and 8.2 minutes) which shows glucose intake by the patient as a disturbance to the system at 500 minutes, when glucose intake was 25mg/dl, patient’s model was restored to steady state using AIBC and IBC at 540 minutes and 565 minutes respectively, while at 45mg/dl it was restored at 578.2 minutes and 590.4 minutes for AIBC and IBC respectively, showing the superiority of developed controller.


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Abu-Rmileh, A., Garcia-Gabin, W., and Zambrano, D. (2010). Internal model sliding mode control approach for glucose regulation in type 1 diabetes. Biomedical Signal Processing and Control, 5(2), 94-102.

Afrand, P., Yazdani, N., Moetamedzadeh, H., Naderi, F., and Panahi, M. (2012). Design and implementation of an expert clinical system for diabetes diagnosis. Global Journal of Science, Engineering and Technology, 3, 23-31.

Ahmad, Munir, F., and Munir, M. F. (2019). An adaptive backstepping based non-linear controller for artificial pancreas in type 1 diabetes patients. Biomedical Signal Processing and Control, 47, 49-56. doi:10.1016/j.bspc.2018.07.016

Ahmad, S., Ahmed, N., Ilyas, M., and Khan, W. (2017). Super twisting sliding mode control algorithm for developing artificial pancreas in type 1 diabetes patients. Biomedical Signal Processing and Control, 38, 200-211.

Alagoz, B. B., Deniz, F. N., Keles, C., and Tan, N. (2015). Disturbance rejection performance analyses of closed loop control systems by reference to disturbance ratio. ISA transactions, 55, 63-71.

Alam, W., Ali, N., Ahmad, S., and Iqbal, J. (2018). Super twisting control algorithm for blood glucose regulation in type 1 diabetes patients. Paper presented at the 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST).

Alphinas, R. A., Hansen, H. H., and Tambo, T. (2017). Comparison of conventional closed-loop controller with an adaptive controller for a disturbed thermodynamic system. Paper presented at the 2017 Evolving and Adaptive Intelligent Systems (EAIS).

Amiri, N., Ramirez-Serrano, A., and Davies, R. J. (2013). Integral backstepping control of an unconventional dual-fan unmanned aerial vehicle. Journal of Intelligent & Robotic Systems, 69(1-4), 147-159.

Babar, S. A., Rana, I. A., Arslan, M., and Zafar, M. W. (2019). Integral Backstepping Based Automated Control of Blood Glucose in Diabetes Mellitus Type 1 Patients. IEEE Access, 7, 173286-173293.

Boiroux, D., Bátora, V., Hagdrup, M., Wendt, S. L., Poulsen, N. K., Madsen, H., and Jørgensen, J. B. (2018). Adaptive model predictive control for a dual-hormone artificial pancreas. Journal of Process Control, 68, 105-117.

Butt, R. S., Ahmad, I., Iftikhar, R., and Arsalan, M. (2019). Integral Backstepping and Synergetic Control for Tracking of Infected Cells During Early Antiretroviral Therapy. IEEE Access, 7, 69447-69455.

Carrasco, D. S., Fu, Y., Goodwin, G. C., King, B. R., and Medioli, A. M. (2014). Performance limitations arising in closed loop control of blood glucose in type 1 diabetes. IFAC Proceedings Volumes, 47(3), 2082-2087.

Cobelli, C., Renard, E., and Kovatchev, B. (2011). Artificial pancreas: past, present, future. Diabetes, 60(11), 2672-2682.

Colmegna, P., Garelli, F., De Battista, H., and Sánchez-Peña, R. (2018). Automatic regulatory control in type 1 diabetes without carbohydrate counting. Control engineering practice, 74, 22-32.

Cooke, D. W., and Plotnick, L. (2008). Type 1 diabetes mellitus in pediatrics. pediatr Rev, 29(11), 374-384.

Cormerais, H., and Richard, P.-Y. (2012). Artificial pancreas for type 1 diabetes: closed-loop algorithm based on error dynamics shaping. Journal of Process Control, 22(7), 1219-1227

Ehsani, M. (2007). Adaptive control of servo motor by MRAC method. Paper presented at the 2007 IEEE Vehicle Power and Propulsion Conference.

Goharimanesh, M., Lashkaripour, A., Shariatnia, S., and Akbari, A. (2014). Diabetic control using genetic fuzzy-PI controller. International Journal of Fuzzy Systems, 16(2), 133-139.

Haidar, A., Smaoui, M. R., Legault, L., and Rabasa-Lhoret, R. (2016). The role of glucagon in the artificial pancreas. The lancet Diabetes & endocrinology, 4(6), 476-479.

Hu, R., and Li, C. (2015). A fuzzy-PID glucose control strategy for insulin therapy in type 1 diabetics. Paper presented at the 2015 IEEE International Conference on Mechatronics and Automation (ICMA).

Khaqan A., Nauman.A.,Shuja. A, Khurshaid. A and Kim, K. (2022). An intelligent model-based effective approach for glycemic control in type I diabetes. World Academy of Science, Engineering and Technology, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 8(11), 779-783.

Kumareswaran, K., Evans, M. L., and Hovorka, R. (2009). Artificial pancreas: an emerging approach to treat Type 1 diabetes. Expert review of medical devices, 6(4), 401-410.

León-Vargas, F., Garelli, F., De Battista, H., and Vehí, J. (2015). Postprandial response improvement via safety layer in closed-loop blood glucose controllers. Biomedical Signal Processing and Control, 16, 80-87.

Lunze, K., Singh, T., Walter, M., Brendel, M. D., and Leonhardt, S. (2013). Blood glucose control algorithms for type 1 diabetic patients: A methodological review. Biomedical Signal Processing and Control, 8(2), 107-119.

Mahmud, F., Isse, N. H., Daud, N. A. M., and Morsin, M. (2017). Evaluation of PD/PID controller for insulin control on blood glucose regulation in a Type-I diabetes. Paper presented at the AIP Conference Proceedings.

Malik.S, Perdeshi, S and Jaya, N. (2019). Design of adaptive controller based on Lyapunov stability for a CSTR. World Academy of Science, Engineering and Technology International Journal of Electronics and Communication Engineering, 8(1), 176-179.

Marchetti, G., Barolo, M., Jovanovic, L., Zisser, H., and Seborg, D. E. (2008). An improved PID switching control strategy for type 1 diabetes. ieee transactions on biomedical engineering, 55(3), 857-865.

Melling, C., Grisé, K., Hasilo, C., Fier, B., Milne, K., Karmazyn, M., and Noble, E. (2013). A model of poorly controlled type 1 diabetes mellitus and its treatment with aerobic exercise training. Diabetes & metabolism, 39(3), 226-235.

Munir, F., Ahmad, I., and Naz, N. (2016).). Backstepping based automatic blood glucose nonlinear controller for diabetes mellitus type 1 patients. Advanced Science Letters, 22(10), 2652-2656. doi:https://doi.org/10.1166/asl.2016.7011

Nath, A., Deb, D., Dey, R., and Das, S. (2018). Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control-based approach. IET systems biology, 12(5), 219-225.

Nath, A., Dey, R., and Aguilar-Avelar, C. (2019). Observer based nonlinear control design for glucose regulation in type 1 diabetic patients: An LMI approach. Biomedical Signal Processing and Control, 47, 7-15.

Ogata, K. (2010). Modern control engineering: Prentice hall.

Organization, W. H. (2000). General guidelines for methodologies on research and evaluation of traditional medicine. Retrieved from

Paiva, H. M., Keller, W. S., and da Cunha, L. G. R. (2020). Blood-Glucose Regulation Using Fractional-Order PID Control. Journal of Control, Automation and Electrical Systems, 31(1), 1-9.

Pankaj, S., Kumar, J. S., and Nema, R. (2011). Comparative analysis of MIT rule and Lyapunov rule in model reference adaptive control scheme. Innovative Systems Design and Engineering, 2(4), 154-162.

Parsa, N. T., Vali, A., and Ghasemi, R. (2014). 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(11), 779-783.

Patel, D., Kumar, R., Laloo, D., and Hemalatha, S. (2012). Natural medicines from plant source used for therapy of diabetes mellitus: An overview of its pharmacological aspects. Asian Pacific Journal of Tropical Disease, 2(3), 239-250.

Pietropaolo, M., and Le Roith, D. (2001). Pathogenesis of diabetes: our current understanding. Clinical cornerstone, 4(2), 1-16.

Ronald Eric, Akif Rahmatillah and Riries Rulaningtyas (2023). Control system modeling of artificial pancreas based on PID controller mechanism with Particle Swarm Optimization (PSO) tuning method: A methodological review. Biomedical Signal Processing and Control, 8(2), 107-119.

Saleem, M. U., Farman, M., Ahmad, M., and Rizwan, M. (2017). Control of an artificial human pancreas. Chinese journal of physics, 55(6), 2273-2282.

Sapiee, M., Selamat, H., Noordin, A., and Jahari, A. (2008). PI Controller design using model reference adaptive control approaches for a chemical process. Paper presented at the Proceedings of 2008 Student Conference on Research and Development (SCOReD 2008).

Shekhar, A., and Sharma, A. (2018). Review of model reference adaptive control. Paper presented at the 2018 International Conference on Information, Communication, Engineering and Technology (ICICET).

Singh, D., Patil, Y., Chougale, K., and Perdeshi, S. (2018). Optimal based PID control scheme for artificial pancreas system. International Journal of Innovative Studies in Sciences and Engineering Technology (IJISSET), 3(4), 23-27.

Stage, E., Ronneby, H., and Damm, P. (2003). Lifestyle change after gestational diabete. Diabetes research and clinical practice, 63(1), 67-72. doi:https://doi.org/10.1016/j.diabres.2003.08.009

Swarnkar, P., Jain, S., and Nema, R. (2010). Effect of adaptation gain on system performance for model reference adaptive control scheme using MIT rule. World Academy of science, engineering and technology, 70, 621-626.

Sylvester, D. D., and Munje, R. K. (2017). Back stepping SMC for blood glucose control of type-1 diabetes mellitus patients. A methodological review. Biomedical Signal Processing and Control, 8(2), 107-120.

Thupili, V. R., and Srinivasan, K. (2009). Multi model based controller design for type 1 diabetic patients. Paper presented at the 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

Umapathy, C. (2011). The State and Future of Closed Loop Insulin Pumps/Artificial Pancreas. Case Western Reserve University.

Vandana Dubey, Harsh Goud and Prakash Chandra Sharma (2021). Comparative analysis of PID tuning techniques for blood glucose level in type 1 diabetes patients. Turkish Journal of Computer and Mathematics Education, Volumes 11(2021), 2948-2953


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