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Modeling of glucose concentration dynamics for predictive control of insulin administration

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The compartmental models, as Hovorka's one, are usually exact but complicated. Thus, they are not suitable for direct usage in nonlinear predictive controllers because of complexity of the resulting controller and numerical problems that may occur. Thus, simplified nonlinear (neural and fuzzy) models are developed in this paper for the future use in the predictive algorithms. Training and structure selection issues are discussed in the context of neural models. The heuristic, easy to obtain, Takagi-Sugeno fuzzy model composed of the control plant step responses is also designed. It is shown that in case of the considered biological process both nonlinear models have significantly better approximation abilities than linear ones.
Twórcy
autor
autor
  • Institute of Radioelectronics, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland, D.Radomski@ire.pw.edu.pl
Bibliografia
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  • 4. Dua P., Doyle III F.J., Pistikopoulos E.N.: Model-based blood glucose control for type 1 diabetes via parametric programming. IEEE Trans. Biomed. Eng. 2006, 53, 8.
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  • 7. Fabietti P.G., Canonico V., Federici M.O., Benedetti M.M., Sarti E.: Control oriented model of insulin and glucose dynamics in type 1 diabetics. Med. Biol. Eng. Comput. 2006, 44, 69-78.
  • 8. Hovorka R., Canonico V., Chassin L.J., Haueter U., Massi-Benedetti M., Orsini M. Federici M.O., Pieber T.R., Schaller H.C., Schaupp L., Vering T., Wilinska M.E.: Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol. Meas. 2004, 25, 905-920.
  • 9. Maciejowski J.M.: Predictive control with constraints, Prentice Hall, Harlow 2002.
  • 10. Rossiter J.A.: Model-based predictive control, CRC Press, Boca Raton 2003.
  • 11. Tatjewski P.: Advanced control of industrial processes, Structures and algorithms, Springer, London 2007.
  • 12. Haykin S.: Neural networks - a comprehensive foundation, Prentice Hall, Englewood Cliffs 1999.
  • 13. Piegat A.: Fuzzy Modeling and Control, Physica-Verlag, Berlin 2001.
  • 14. Takagi T., Sugeno M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Sys. Man Cybern. 1985, 15, 116-132.
  • 15. Tatjewski P., Ławryńczuk M.: Soft computing in model-based predictive control. Int. J. Appl. Math. Comp. Sci. 2006, 16, 101-120.
  • 16. Bazaraa M.S., Sherali J., Shetty K.: Nonlinear programming: theory and algorithms, Prentice Hall, 1999.
  • 17. Marusak P.: Advantages of an easy to design fuzzy predictive algorithm: application to a nonlinear chemical reactor, In: Proc. Int. Multiconf. on Computer Science and Information Technology, Wisła, Poland 2007.
  • 18. Parker R.S., Gatzke E.P., Doyle F.J.: Advanced Model Predictive Control (MPC) for type I diabetic patient blood glucose control, In: Proc. American Control Conference, Chicago, USA, 2000, 3483-3487.
  • 19. Trajanoski Z.: Neural predictive controller for insulin delivery using the subcutaneous route. IEEE Trans. Biomed. Eng. 1998, 45, 1122-1134.
  • 20. Ławryńczuk M.: A family of model predictive control algorithms with artificial neural networks. Int. J. Appl. Math. Comp. Sci. 2007, 17, 217-232.
  • 21. Campos-Delgado D.U., Hernandez-Ordonez M., Gordillo-Moscose A.: Fuzzy-based controller for glucose regulation in type-1 diabetic patients by subcutaneous route. IEEE Trans. Biomed. Eng. 2006, 53, 2201-2210.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0059-0009
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