Back Stepping Sliding Mode Control of Blood Glucose for Type I Diabetes
Diabetes is a growing health problem in worldwide.
Especially, the patients with Type 1 diabetes need strict glycemic
control because they have deficiency of insulin production. This
paper attempts to control blood glucose based on body mathematical
body model. The Bergman minimal mathematical model is used to
develop the nonlinear controller. A novel back-stepping based sliding
mode control (B-SMC) strategy is proposed as a solution that
guarantees practical tracking of a desired glucose concentration. In
order to show the performance of the proposed design, it is compared
with conventional linear and fuzzy controllers which have been done
in previous researches. The numerical simulation result shows the
advantages of sliding mode back stepping controller design to linear
and fuzzy controllers.
[1] K. Eskaf, O. Badawi, and T. Ritchings, “Predicting Blood Glucose
Levels in Diabetics Using Feature Extraction and Artificial Neural
Networks,” in Proc. of Int. Conf. on Information and Communication
Technologies: From Theory to Applications, Damascus, Syria, 2008, pp.
1-6.
[2] O. Vega-Hernandez, D. U. Campos-Delgado, and D. R. Espinoza Trejo,
“Increasing security in an artificial pancreas: diagnosis of actuator
faults” in Proc. of Pan-American Health Care Exchanges Conference,
Mexico City, Mexico, 2009, pp. 137-142.
[3] V. R. Kondepati and H. M. Heise, “Recent progress in analytical
instrumentation for glycemic control in diabetic and critically ill
patients” Anal Bional Chem, 388, pp. 545-563, 2007.
[4] Thomson, S., D. Beaven, M. Jamieson, S. Snively, A.Howl and A.
Christophersen, "Type II diabetes: Managing for better health
outcomes". in Price Water House Coopers Report, Diabetes New
Zealand Inc. 2001.
[5] Topp, B., K. Promislow and G. De Vries, "A model of $-cell mass,
insulin and glucose kinetics:"Pathways to diabetes. J. Theor. Bio , 2000,
pp.605-619.
[6] S. M. Lynch and B. W. Bequette, “Estimation-based model predictive
control of blood glucose in type I diabetics: A simulation study “,
Proceedings of the IEEE 27th Annual Northeast Bioengineering
Conference, Storrs, CT, USA, 2001, pp. 79-80.
[7] Bergman, R.N., L.S. Philips and C. Cobelli, "Physiologic evaluation of
factors controlling glucose tolerance in man". J. Clin. Invest, 1981, pp.
1456-1467.
[8] Li, C. and R. Hu, "PID Control Based on BP Neural Network for the
Regulation of Blood Glucose Level in Diabetes", in Proceeding of the
7th IEEE International Conference on Bioinformatics and
Bioengineering, Boston, 2007, pp. 1168-1172.
[9] Chee F, Fernando TL, Savkin AV, Heeden VV. "Expert PID control
system for blood glucose control in critically ill patients." IEEE
Transactions on Information Technology in Biomedicine, 2003, pp.419–
425.
[10] Salzsieder E, Albrecht G, Fischer U, Freyse E-J. "Kinetic modeling of
the glucoregulatory system to improve insulin therapy". IEEE
Transactions on Biomedical Engineering, 1985; vol.32, pp.846–855.
[11] Parker RS, Doyle III FJ, Peppas NA. "A model-based algorithm for
blood glucose control in type I diabetic patients". IEEE Transactions on
Biomedical Engineering; vol.46, No.2, 1999, pp.148–157.
[12] Lynch SM, Bequette BW. "Model predictive control of blood glucose in
type I diabetes using subcutaneous glucose measurements", in
Proceedings of the American Control Conference, Anchorage, AK,
U.S.A, May 2002, pp. 4039–4040.
[13] Li, C. and R. Hu,. "PID Control Based on BP Neural Network for the
Regulation of Blood Glucose Level in Diabetes", in the 7th IEEE
International Conference on Bioinformatics and Bioengineering,
Boston, 2007, pp.1168-1172.
[14] Beyki, K., M. Dashti Javan, K. Shojaee and M.M. Neshati, 2010. "An
intelligent approach for optimal regulation of blood glucose level." In
17th Iranian Conference of Biomedical Engineering. pp: 1-5.
[15] Yasini, S., M.B. Naghibi-Sistani and A. Karimpour, "Active insulin
infusion using fuzzy-based closedloop control", in 3rd International
Conference on Intelligent System and Knowledge Engineering,
Mashhad, Iran, 2008, pp: 429-434.
[16] D. N. Maryam Abadi ,A. Alfi ,M. Siahi, "An Improved Fuzzy PI
Controller for Type 1 Diabetes", Research Journal of Applied Sciences,
Engineering and Technology , vol.4, No.21, 2012, pp. 4417-4422.
[17] Parker RS, Peppas NA, Doyle III FJ. "Blood glucose control in type I
diabetic patients using the intravenous route". IEE Journal of
Engineering in Medicine and Biology, 2001, 20:65–73.
[18] Parker RS, Ward JH, Peppas NA, Doyle III FJ. "Robust H∞ control in
diabetes using a physiological model." AlChE Journal, vol.46, No.12,
2000, pp.2537–2545.
[19] Chee F, Savkin AV, Fernando TL, Nahavandi S. "OptimalH-infinity
insulin injection control for blood glucose regulation in diabetic
patients", IEEE Transactions on Biomedical Engineering , vol.52,
No.10, 2005, pp.1625 1631.
[20] Mine M. Ozyetkin ,N Nath ,E Tatlicioglu, Darren M. Dawson, "A New
Robust Nonlinear Control Algorithm for the Regulation of Blood
Glucose in Diabetic Patients" in IEEE International Conference on
Control Applications (CCA) part of 2012 IEEE Multi Conference on
Systems and Control October 3-5, 2012. Dubrovnik, Croatia
[21] Kaveh, P. and Shtessel, Y. B, "Blood glucose regulation using higherorder
sliding mode control." Int. J. Robust Nonlinear Control, vol.18,
2008, pp. 557–569.
[22] Bolie, V.W, "Coefficients of normal blood glucose regulation". J. Appl.
Physiol, 16, 1961, pp. 783-788.
[23] Makrogluo, A., J. Li and Y. Kuang, "Mathematical models and software
tools for the glucose-insulin regulatory system and diabetes: An
overview". Appl. Numerical Math, 56: 559-573, 2006.
[24] Ahab, A., Y.K. Kong and C. Quek. "Model reference adaptive control
on glucose for the treatment of diabetes mellitus". 19th IEEE Symposium
on Computer-Based Medical Systems, 2006, pp. 315-320.
[25] Fisher ME. "A semi closed-loop algorithm for the control of blood
glucose levels in diabetics". IEEE Transactions on Biomedical
Engineering, vol.38, No.1, 1991, pp.57–61.
[26] L. Fridman, J. Moreno, R. Iriarte, "Sliding Modes after the First Decade
of the 21st Century", Springer, 2011.
[1] K. Eskaf, O. Badawi, and T. Ritchings, “Predicting Blood Glucose
Levels in Diabetics Using Feature Extraction and Artificial Neural
Networks,” in Proc. of Int. Conf. on Information and Communication
Technologies: From Theory to Applications, Damascus, Syria, 2008, pp.
1-6.
[2] O. Vega-Hernandez, D. U. Campos-Delgado, and D. R. Espinoza Trejo,
“Increasing security in an artificial pancreas: diagnosis of actuator
faults” in Proc. of Pan-American Health Care Exchanges Conference,
Mexico City, Mexico, 2009, pp. 137-142.
[3] V. R. Kondepati and H. M. Heise, “Recent progress in analytical
instrumentation for glycemic control in diabetic and critically ill
patients” Anal Bional Chem, 388, pp. 545-563, 2007.
[4] Thomson, S., D. Beaven, M. Jamieson, S. Snively, A.Howl and A.
Christophersen, "Type II diabetes: Managing for better health
outcomes". in Price Water House Coopers Report, Diabetes New
Zealand Inc. 2001.
[5] Topp, B., K. Promislow and G. De Vries, "A model of $-cell mass,
insulin and glucose kinetics:"Pathways to diabetes. J. Theor. Bio , 2000,
pp.605-619.
[6] S. M. Lynch and B. W. Bequette, “Estimation-based model predictive
control of blood glucose in type I diabetics: A simulation study “,
Proceedings of the IEEE 27th Annual Northeast Bioengineering
Conference, Storrs, CT, USA, 2001, pp. 79-80.
[7] Bergman, R.N., L.S. Philips and C. Cobelli, "Physiologic evaluation of
factors controlling glucose tolerance in man". J. Clin. Invest, 1981, pp.
1456-1467.
[8] Li, C. and R. Hu, "PID Control Based on BP Neural Network for the
Regulation of Blood Glucose Level in Diabetes", in Proceeding of the
7th IEEE International Conference on Bioinformatics and
Bioengineering, Boston, 2007, pp. 1168-1172.
[9] Chee F, Fernando TL, Savkin AV, Heeden VV. "Expert PID control
system for blood glucose control in critically ill patients." IEEE
Transactions on Information Technology in Biomedicine, 2003, pp.419–
425.
[10] Salzsieder E, Albrecht G, Fischer U, Freyse E-J. "Kinetic modeling of
the glucoregulatory system to improve insulin therapy". IEEE
Transactions on Biomedical Engineering, 1985; vol.32, pp.846–855.
[11] Parker RS, Doyle III FJ, Peppas NA. "A model-based algorithm for
blood glucose control in type I diabetic patients". IEEE Transactions on
Biomedical Engineering; vol.46, No.2, 1999, pp.148–157.
[12] Lynch SM, Bequette BW. "Model predictive control of blood glucose in
type I diabetes using subcutaneous glucose measurements", in
Proceedings of the American Control Conference, Anchorage, AK,
U.S.A, May 2002, pp. 4039–4040.
[13] Li, C. and R. Hu,. "PID Control Based on BP Neural Network for the
Regulation of Blood Glucose Level in Diabetes", in the 7th IEEE
International Conference on Bioinformatics and Bioengineering,
Boston, 2007, pp.1168-1172.
[14] Beyki, K., M. Dashti Javan, K. Shojaee and M.M. Neshati, 2010. "An
intelligent approach for optimal regulation of blood glucose level." In
17th Iranian Conference of Biomedical Engineering. pp: 1-5.
[15] Yasini, S., M.B. Naghibi-Sistani and A. Karimpour, "Active insulin
infusion using fuzzy-based closedloop control", in 3rd International
Conference on Intelligent System and Knowledge Engineering,
Mashhad, Iran, 2008, pp: 429-434.
[16] D. N. Maryam Abadi ,A. Alfi ,M. Siahi, "An Improved Fuzzy PI
Controller for Type 1 Diabetes", Research Journal of Applied Sciences,
Engineering and Technology , vol.4, No.21, 2012, pp. 4417-4422.
[17] Parker RS, Peppas NA, Doyle III FJ. "Blood glucose control in type I
diabetic patients using the intravenous route". IEE Journal of
Engineering in Medicine and Biology, 2001, 20:65–73.
[18] Parker RS, Ward JH, Peppas NA, Doyle III FJ. "Robust H∞ control in
diabetes using a physiological model." AlChE Journal, vol.46, No.12,
2000, pp.2537–2545.
[19] Chee F, Savkin AV, Fernando TL, Nahavandi S. "OptimalH-infinity
insulin injection control for blood glucose regulation in diabetic
patients", IEEE Transactions on Biomedical Engineering , vol.52,
No.10, 2005, pp.1625 1631.
[20] Mine M. Ozyetkin ,N Nath ,E Tatlicioglu, Darren M. Dawson, "A New
Robust Nonlinear Control Algorithm for the Regulation of Blood
Glucose in Diabetic Patients" in IEEE International Conference on
Control Applications (CCA) part of 2012 IEEE Multi Conference on
Systems and Control October 3-5, 2012. Dubrovnik, Croatia
[21] Kaveh, P. and Shtessel, Y. B, "Blood glucose regulation using higherorder
sliding mode control." Int. J. Robust Nonlinear Control, vol.18,
2008, pp. 557–569.
[22] Bolie, V.W, "Coefficients of normal blood glucose regulation". J. Appl.
Physiol, 16, 1961, pp. 783-788.
[23] Makrogluo, A., J. Li and Y. Kuang, "Mathematical models and software
tools for the glucose-insulin regulatory system and diabetes: An
overview". Appl. Numerical Math, 56: 559-573, 2006.
[24] Ahab, A., Y.K. Kong and C. Quek. "Model reference adaptive control
on glucose for the treatment of diabetes mellitus". 19th IEEE Symposium
on Computer-Based Medical Systems, 2006, pp. 315-320.
[25] Fisher ME. "A semi closed-loop algorithm for the control of blood
glucose levels in diabetics". IEEE Transactions on Biomedical
Engineering, vol.38, No.1, 1991, pp.57–61.
[26] L. Fridman, J. Moreno, R. Iriarte, "Sliding Modes after the First Decade
of the 21st Century", Springer, 2011.
@article{"International Journal of Medical, Medicine and Health Sciences:68245", author = "N. Tadrisi Parsa and A. R. Vali and R. Ghasemi", title = "Back Stepping Sliding Mode Control of Blood Glucose for Type I Diabetes", abstract = "Diabetes is a growing health problem in worldwide.
Especially, the patients with Type 1 diabetes need strict glycemic
control because they have deficiency of insulin production. This
paper attempts to control blood glucose based on body mathematical
body model. The Bergman minimal mathematical model is used to
develop the nonlinear controller. A novel back-stepping based sliding
mode control (B-SMC) strategy is proposed as a solution that
guarantees practical tracking of a desired glucose concentration. In
order to show the performance of the proposed design, it is compared
with conventional linear and fuzzy controllers which have been done
in previous researches. The numerical simulation result shows the
advantages of sliding mode back stepping controller design to linear
and fuzzy controllers.
", keywords = "Back stepping, Bergman Model, Nonlinear control,
Sliding mode control.", volume = "8", number = "11", pages = "776-5", }
