Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 281589, 8 pages
http://dx.doi.org/10.1155/2015/281589
Research Article

An Improved PID Algorithm Based on Insulin-on-Board Estimate for Blood Glucose Control with Type 1 Diabetes

School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China

Received 16 April 2015; Revised 27 May 2015; Accepted 2 June 2015

Academic Editor: Tao Huang

Copyright © 2015 Ruiqiang Hu and Chengwei Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Li, Y. Kuang, and C. C. Mason, “Modeling the glucose-insulin regulatory system and ultradian insulin secretory oscillations with two explicit time delays,” Journal of Theoretical Biology, vol. 242, no. 3, pp. 722–735, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. http://www.idf.org/.
  3. G. Marchetti, M. Barolo, L. Jovanovic, H. Zisser, and D. E. Seborg, “An improved PID switching control strategy for type 1 diabetes,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 3, pp. 857–865, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. H. Zisser, L. Robinson, W. Bevier et al., “Bolus calculator: a review of four ‘smart’ insulin pumps,” Diabetes Technology and Therapeutics, vol. 10, no. 6, pp. 441–444, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Mythreyi, S. C. Subramanian, and R. Krishna Kumar, “Nonlinear glucose-insulin control considering delays-Part II: control algorithm,” Control Engineering Practice, vol. 28, no. 1, pp. 26–33, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. E. M. Watson, M. J. Chappell, F. Ducrozet, S. M. Poucher, and J. W. T. Yates, “A new general glucose homeostatic model using a proportional-integral-derivative controller,” Computer Methods and Programs in Biomedicine, vol. 102, no. 2, pp. 119–129, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Lee and B. W. Bequette, “A closed-loop artificial pancreas based on model predictive control: human-friendly identification and automatic meal disturbance rejection,” Biomedical Signal Processing and Control, vol. 4, no. 4, pp. 347–354, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Magni, D. M. Raimondo, C. Dalla Man, G. De Nicolao, B. Kovatchev, and C. Cobelli, “Model predictive control of glucose concentration in type I diabetic patients: an in silico trial,” Biomedical Signal Processing and Control, vol. 4, no. 4, pp. 338–346, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. I. Y. S. Chávez, R. Morales-Menéndez, and S. O. M. Chapa, “Glucose optimal control system in diabetes treatment,” Applied Mathematics and Computation, vol. 209, no. 1, pp. 19–30, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. M. Ottavian, M. Barolo, H. Zisser, E. Dassau, and D. E. Seborg, “Adaptive blood glucose control for intensive care applications,” Computer Methods and Programs in Biomedicine, vol. 109, no. 2, pp. 144–156, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. A. G. Gallardo Hernández, L. Fridman, A. Levant et al., “High-order sliding-mode control for blood glucose: practical relative degree approach,” Control Engineering Practice, vol. 21, no. 5, pp. 747–758, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. E. Ackerman, L. C. Gatewood, J. W. Rosevear, and G. D. Molnar, “Model studies of blood-glucose regulation,” The Bulletin of Mathematical Biophysics, vol. 27, no. 1, pp. 21–37, 1965. View at Publisher · View at Google Scholar · View at Scopus
  13. R. N. Bergman, L. S. Phillips, and C. Cobelli, “Physiologic evaluation of factors controlling glucose tolerance in man. Measurement of insulin sensitivity and β-cell glucose sensitivity from the response to intravenous glucose,” Journal of Clinical Investigation, vol. 68, no. 6, pp. 1456–1467, 1981. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Cobelli and A. Mari, “Validation of mathematical models of complex endocrine-metabolic systems. A case study on a model of glucose regulation,” Medical and Biological Engineering and Computing, vol. 21, no. 4, pp. 390–399, 1983. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Hovorka, V. Canonico, L. J. Chassin et al., “Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes,” Physiological Measurement, vol. 25, no. 4, pp. 905–920, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. C. Dalla Man, M. Camilleri, and C. Cobelli, “A system model of oral glucose absorption validation on gold standard data,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, pp. 2472–2478, 2006. View at Publisher · View at Google Scholar
  17. X. Gao, H. Ning, and Y. Wang, “Systematically in silico comparison of unihormonal and bihormonal artificial pancreas systems,” Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 712496, 10 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  18. C. Ellingsen, E. Dassau, H. Zisser et al., “Safety constraints in an artificial pancreatic β cell: an implementation of model predictive control with insulin on board,” Journal of Diabetes Science and Technology, vol. 3, no. 3, pp. 536–544, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. F. León-Vargas, F. Garelli, H. De Battista, and J. Vehí, “Postprandial blood glucose control using a hybrid adaptive PD controller with insulin-on-board limitation,” Biomedical Signal Processing and Control, vol. 8, no. 6, pp. 724–732, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. B. P. Kovatchev, W. L. Clarke, M. Breton, K. Brayman, and A. McCall, “Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application,” Diabetes Technology & Therapeutics, vol. 7, no. 6, pp. 849–862, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Magni, D. M. Raimondo, C. Dalla Man et al., “Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis,” Journal of Diabetes Science and Technology, vol. 2, no. 4, pp. 630–635, 2008. View at Publisher · View at Google Scholar · View at Scopus