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Glucose Regulation in Diabetes Patients Via Insulin Pump: A Feedback Linearisation Approach

  • Sipon DasEmail author
  • Anirudh Nath
  • Rajeeb Dey
  • Saurabh Chaudhury
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 757)

Abstract

The primary objective of the paper is to design a nonlinear control technique for a nonlinear intravenous model of Type 1 diabetes mellitus (T1DM) patient. Input–output feedback linearisation is utilised for deriving the nonlinear control law based on a modified version of Bergman’s minimal model augmented with the dynamics of the insulin pump and the meal disturbance. The results depict that the proposed control technique avoids severe hypoglycaemia and postprandial hyperglycaemia in the presence of exogenous meal disturbance as well as parametric uncertainty within a population of 100 virtual T1DM patients (inter-patient variability). The efficacy of the proposed control technique is investigated through variability grid analysis.

Keywords

Type 1 diabetes mellitus Hypoglycaemia Feedback linearisation Inter-patient variability 

Notes

Acknowledgements

Authors acknowledge the financial support by TEQIP-III, NIT Silchar, 788010, Assam India for this work.

References

  1. 1.
    Ahmad, S., Ahmed, N., Ilyas, M., Khan, W., et al.: Super twisting sliding mode control algorithm for developing artificial pancreas in type 1 diabetes patients. Biomed. Signal Process. Control 38, 200–211 (2017)CrossRefGoogle Scholar
  2. 2.
    Ali, S.F., Padhi, R.: Optimal blood glucose regulation of diabetic patients using single network adaptive critics. Potimal Control Appl. Methods 32(2), 196–214 (2011)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bequette, B.W., Cameron, F., Buckingham, B.A., Maahs, D.M., Lum, J.: Overnight hypoglycemia and hyperglycemia mitigation for individuals with type 1 diabetes: how risks can be reduced. IEEE Control Syst. 38(1), 125–134 (2018)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bergmann, R.: Physiologic evaluation of factors controlling glucose tolerance in man. J. Clin. Invest. 68, 1456–1467 (1981)CrossRefGoogle Scholar
  5. 5.
    Bondia, J., Romero-Vivo, S., Ricarte, B., Diez, J.L.: Insulin estimation and prediction: a review of the estimation and prediction of subcutaneous insulin pharmacokinetics in closed-loop glucose control. IEEE Control Syst. 38(1), 47–66 (2018)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Cobelli, C., Dalla, C., Man, G.S.: Diabetes: Models, signals, and control. IEEE Rev. Biomedi. Eng. 2(3), 54–96 (2009)CrossRefGoogle Scholar
  7. 7.
    Cinar, A.: Artificial pancreas systems: an introduction to the special issue. IEEE Control Sys. 38(1), 26–29 (2018)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Cocha, G., Amorena, C., Mazzadi, A., D’Attellis, C.: Geometric adaptive control in type 1 diabetes. In: 12th International Symposium on Medical Information Processing and Analysis, vol. 10160, p. 101600R. International Society for Optics and Photonics (2017)Google Scholar
  9. 9.
    Coman, S., Boldisor, C.: Simulation of an adaptive closed loop system for blood glucose concentration control. Bulletin of the Transilvania University of Brasov. Eng. Sci. Series I 8(2), 107 (2015)Google Scholar
  10. 10.
    Fischer, U., Schenk, W., Salzsieder, E., Albrecht, G., Abel, P., Freyse, E.J.: Does physiological blood glucose control require an adaptive control strategy? IEEE Trans. Biomed. Eng. 8, 575–582 (1987)CrossRefGoogle Scholar
  11. 11.
    Haidar, A.: The artificial pancreas: how closed-loop control is revolutionizing diabetes. IEEE Control Syst. 36(5), 28–47 (2016)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hariri, A., Wang, L.Y.: Observer-based state feedback for enhanced insulin control of type i sdiabetic patients. Open Biomed. Eng. J. 5, 98 (2011)CrossRefGoogle Scholar
  13. 13.
    Hariri, A.M.: Identification, state estimation, and adaptive control of type i diabetic patients (2011)Google Scholar
  14. 14.
    Hernandez, A.G.G., Fridman, L., Levant, A., Shtessel, Y., Leder, R., Monsalve, C.R., Andrade, S.I.: High-order sliding-mode control for blood glucose: practical relative degree approach. Control Eng. Practice 21(5), 747–758 (2013)CrossRefGoogle Scholar
  15. 15.
    Hovorka, R., Svačina, Š., Carson, E.R., Williams, C.D., Sänksen, P.H.: A consultation system for insulin therapy. Comput. Methods Programs Biomed. 32(3), 303–310 (1990).  https://doi.org/10.1016/0169-2607(90)90113-NCrossRefGoogle Scholar
  16. 16.
    Kaveh, P., Shtessel, Y.B.: Blood glucose regulation using higher-order sliding mode control. Int. J. Rob. Nonlinear Control 18(4–5), 557–569 (2008)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Khalil, H.K., Grizzle, J.: Nonlinear Systems, vol. 3. Prentice hall Upper Saddle River (2002)Google Scholar
  18. 18.
    Magni, L., Raimondo, D.M., Man, C.D., Breton, M., Patek, S., De Nicolao, G., Cobelli, C., Kovatchev, B.P.: Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis. J. Diabetes Sci. Technol. 2(4), 630–635 (2008)CrossRefGoogle Scholar
  19. 19.
    Parsa, N.T., Vali, A., Ghasemi, R.: Back stepping sliding mode control of blood glucose for type i diabetes. World Acad. Sci. Eng. Technol. Int. J. Med. Health Biomed. Bioeng. Pharm. Eng. 8(11), 779–783 (2014)Google Scholar
  20. 20.
    Van Herpe, T., Pluymers, B., Espinoza, M., Van den Berghe, G., De Moor, B.: A minimal model for glycemia control in critically ill patients. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006. EMBS’06 , pp. 5432–5435. IEEE (2006)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Sipon Das
    • 1
    Email author
  • Anirudh Nath
    • 1
  • Rajeeb Dey
    • 1
  • Saurabh Chaudhury
    • 1
  1. 1.National Institute of TechnologSilcharIndia

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