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Blood-Glucose Regulation Using Fractional-Order PID Control

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Abstract

This paper proposes a blood-glucose regulation approach employing a fractional-order proportional-integral-derivative (FOPID) controller, whose parameters are tuned using a numerical optimization methodology. The proposed technique is tested on 100 virtual patients using the Dalla Man model, an in silico type-1 diabetic patient model from the literature. The results are favorably compared with the ones obtained with a standard PID control. In a series of simulated tests, the FOPID approach leads to better results in terms of regulating the blood glucose levels between the specified limits, at the expense of requiring a higher, yet reasonable amount of insulin injected to the patient. Simulations were run for one day, and two different diets were considered. The quality of the regulation was measured in terms of the integral of blood glucose beyond the specified limits of 70 and 180 mg/dl. The values obtained with the PID controller were \(17.5 \pm 18.9\) and \(13.1 \pm 16.8\) min g/dl, while the FOPID controller leads to values of \(7.3 \pm 9.3\) and \(7.0 \pm 8.0\) min g/dl, respectively. On the other hand, the FOPID increased the request amount of insulin, from \(1.9 \pm 1.6\) and \(1.7 \pm 1.5\) nmol/kg to \(3.0 \pm 2.2\) and \(2.7 \pm 2.0\) nmol/kg (still within the expected daily range of 3–6 nmol/kg of insulin).

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References

  • Ahmad, S., Ahmed, N., Ilyas, M., & 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.

    Article  Google Scholar 

  • Ahmad, I., Munir, F., & 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.

    Article  Google Scholar 

  • Astrom, K. J., & Hagglund, T. (1995). PID controllers: Theory, design, and tuning. Research Triangle Park, NC: Instrument society of America.

    Google Scholar 

  • Ates, A., & Yeroglu, C. (2016). Optimal fractional order PID design via Tabu search based algorithm. ISA Transactions, 60, 109–118.

    Article  Google Scholar 

  • Bergman, R. N. (1989). Toward physiological understanding of glucose tolerance: Minimal-model approach. Diabetes, 38(12), 1512–1527.

    Article  Google Scholar 

  • Bhattacharjee, A., Easwaran, A., Leow, M. K. S., & Cho, N. (2018). Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control. Biomedical Signal Processing and Control, 41, 198–209.

    Article  Google Scholar 

  • Bingul, Z., & Karahan, O. (2018). Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay. Optimal Control Applications and Methods, 39(4), 1431–1450.

    Article  MathSciNet  Google Scholar 

  • Biswas, D., Sharma, K. D., & Sarkar, G. (2018). Stable adaptive NSOF domain FOPID controller for a class of non-linear systems. IET Control Theory & Applications, 12(10), 1402–1413.

    Article  MathSciNet  Google Scholar 

  • Boiroux, D., Duun-Henriksen, A. K., Schmidt, S., Nørgaard, K., Madsbad, S., & Poulsen, N. K. (2018). Overnight glucose control in people with type 1 diabetes. Biomedical Signal Processing and Control, 39, 503–512.

    Article  Google Scholar 

  • Cobelli, C., Dalla Man, C., Sparacino, G., Magni, L., De Nicolao, G., & Kovatchev, B. P. (2009). Diabetes: Models, signals, and control. IEEE Reviews in Biomedical Engineering, 2, 54–96.

    Article  Google Scholar 

  • Colmegna, P., Sanchez-Pena, R. S., & Gondhalekar, R. (2018). Linear parameter-varying model to design control laws for an artificial pancreas. Biomedical Signal Processing and Control, 40, 204–213.

    Article  Google Scholar 

  • Dalau, M., Gligor, A., & Dalau, T. M. (2017). Fractional order controllers versus integer order controllers. Procedia Engineering, 181, 538–545.

    Article  Google Scholar 

  • Dalla Man, C., Raimondo, D. M., Rizza, R. A., & Cobelli, C. (2007a). GIM, simulation software of meal glucose–Insulin model. Journal of Diabetes Science and Technology, 1(3), 323–330.

    Article  Google Scholar 

  • Dalla Man, C., Rizza, R. A., & Cobelli, C. (2007b). Meal simulation model of the glucose–insulin system. IEEE Transactions on Biomedical Engineering, 54(10), 1740–1749.

    Article  Google Scholar 

  • Dastjerdi, A. A., Saikumar, N., & HosseinNia, H. (2018). Tuning guidelines for fractional order PID controllers: Rules of thumb. Mechatronics, 56, 26–36.

    Article  Google Scholar 

  • Gondhalekar, R., Dassau, E., & Doyle, F. J., III. (2018). Velocity-weighting and velocity-penalty MPC of an artificial pancreas: Improved safety and performance. Automatica, 91, 105–117.

  • Haidar, A. (2016). The artificial pancreas: How closed-loop control is revolutionizing diabetes. IEEE Control Systems, 36(5), 28–47.

    Article  MathSciNet  Google Scholar 

  • Hirsch, I. B. (1999). Type 1 diabetes mellitus and the use of flexible insulin regimens. American Family Physician, 60(8), 2343–52.

    Google Scholar 

  • Hovorka, R., Chassin, L. J., Ellmerer, M., Plank, J., & Wilinska, M. E. (2008). A simulation model of glucose regulation in the critically ill. Physiological Measurement, 29(8), 959.

    Article  Google Scholar 

  • Kadish, A. H. (1963). Automation control of blood sugar a servomechanism for glucose monitoring and control. ASAIO Journal, 9(1), 363–367.

    Google Scholar 

  • Kadu, C. B., & Patil, C. Y. (2016). Design and implementation of stable PID controller for interacting level control system. Procedia Computer Science, 79, 737–746.

    Article  Google Scholar 

  • Kanderian, S. S., Weinzimer, S., Voskanyan, G., & Steil, G. M. (2009). Identification of intraday metabolic profiles during closed-loop glucose control in individuals with type 1 diabetes. Journal of Diabetes Science and Technology, 3(5), 1047–1057.

    Article  Google Scholar 

  • Lagarias, J. C., Reeds, J. A., Wright, M. H., & Wright, P. E. (1998). Convergence properties of the Nelder–Mead simplex method in low dimensions. SIAM Journal on Optimization, 9(1), 112–147.

    Article  MathSciNet  Google Scholar 

  • Lanusse, P., Sabatier, J., & Oustaloup, A. (2015). Fractional order PID and first generation CRONE control system design. Fractional order differentiation and robust control design (pp. 63–105). Dordrecht: Springer.

    Book  Google Scholar 

  • Lu, L. I. U., Liang, S. H. A. N., Yuewei, D. A. I., Chenglin, L. I. U., & Zhidong, Q. I. (2018). Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller. Journal of Systems Engineering and Electronics, 29(1), 166–175.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mansell, E. J., Docherty, P. D., & Chase, J. G. (2017). Shedding light on grey noise in diabetes modelling. Biomedical Signal Processing and Control, 31, 16–30.

    Article  Google Scholar 

  • Nath, A., Deb, D., Dey, R., & 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.

    Article  Google Scholar 

  • Nath, A., Dey, R., & 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.

    Article  Google Scholar 

  • Podlubny, I. (1994). Fractional-order systems and fractional-order controllers. Bratislava: Institute of Experimental Physics, Slovak Academy of Sciences.

    MATH  Google Scholar 

  • Qu, Y., et al. (2018). Dose unit establishment for a new basal insulin using joint modeling of insulin dose and glycemic response. Journal of Diabetes Science and Technology, 12(1), 155–162.

    Article  Google Scholar 

  • Ramezanian, H., Balochian, S., & Zare, A. (2013). Design of optimal fractional-order PID controllers using particle swarm optimization algorithm for automatic voltage regulator (AVR) system. Journal of Control, Automation and Electrical Systems, 24(5), 601–611.

    Article  Google Scholar 

  • Regittnig, W., Urschitz, M., Lehki, B., Wolf, M., Kojzar, H., Mader, J. K., et al. (2019). Insulin bolus administration in insulin pump therapy: Effect of bolus delivery speed on insulin absorption from subcutaneous tissue. Diabetes Technology and Therapeutics, 21(1), 44–50.

    Article  Google Scholar 

  • Saleem, M. U., Farman, M., Rizwan, M., & Ahmad, M. O. (2018). Controllability and observability of glucose insulin glucagon system in humans. Chinese Journal of Physics, 56(5), 1909–1916.

    Article  Google Scholar 

  • Valerio, D., & Sa da Consta, J. (2013). An introduction to fractional control. The Institution of Engineering and Technology, IET Control Engineering Series, 91, 121–124.

    MathSciNet  Google Scholar 

  • Verma, S. K., Yadav, S., & Nagar, S. K. (2017). Optimization of fractional order PID controller using grey wolf optimizer. Journal of Control, Automation and Electrical Systems, 28(3), 314–322.

    Article  Google Scholar 

  • Zhang, S., Liu, L., & Cui, X. (2019). Robust FOPID controller design for fractional-order delay systems using positive stability region analysis. International Journal of Robust and Nonlinear Control, 29(15), 5195–5212.

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The authors are indebted to Prof. Claudio Cobelli (University of Padova, Italy) for sharing part of the computational code that was used in this work. Furthermore, the authors gratefully acknowledge the suggestions of Prof. Karina Rabello Casali (Federal University of Sao Paulo, UNIFESP, Brazil) and Prof. Karl Heinz Kienitz (Aeronautical Institute of Technology, ITA, Brazil). Finally, the contributions of Mr. Ayrton Casella (UNIFESP) in the first draft of this manuscript are also gratefully acknowledged.

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Correspondence to Henrique Mohallem Paiva.

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Paiva, H.M., Keller, W.S. & da Cunha, L.G.R. Blood-Glucose Regulation Using Fractional-Order PID Control. J Control Autom Electr Syst 31, 1–9 (2020). https://doi.org/10.1007/s40313-019-00552-0

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