Is nonlinear model predictive control with fuzzy predictive model proper for managing the blood glucose level in typeI diabetes?
Affiliation(s)
Young Researcher Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Department of Biomedical Engineering, Tehran University, Tehran, Iran.
Young Researcher Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Department of Biomedical Engineering, Tehran University, Tehran, Iran.
ABSTRACT
In recent decades, due to the increasing risk of diabetes, the measurement and control of the blood sugar is of great importance. In typeI diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose, and thus have a low level of glucose. To control blood glucose, the insulin must be injected to the body. In fact, the injection must be in a completely controlled environment. If the level of the insulin exceeds the physiological limits, it may cause death. This paper presents an online approach to control the blood glucose level using a nonlinear model predictive control. This method, maintains the level of blood glucose concentration within a normal range. Thus, the blood glucose level is measured in each minute and predicted for the next time interval. If that is not in the normal range, amount of the insulin which must be injected will be determined. The proposed control approach includes important features such as model uncertainties and prevents acute decrease in the blood glucose level, and instability. In order to assess performance of the proposed controller, computer simulations have been carried out in Matlab/Simulink. Simulation results will reveal the effectiveness of the proposed nonlinear model predictive controller in adjusting the blood glucose level by injecting required insulin. So if the nutrition of the person decreases instantly, the hypoglycemia does not happen.
In recent decades, due to the increasing risk of diabetes, the measurement and control of the blood sugar is of great importance. In typeI diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose, and thus have a low level of glucose. To control blood glucose, the insulin must be injected to the body. In fact, the injection must be in a completely controlled environment. If the level of the insulin exceeds the physiological limits, it may cause death. This paper presents an online approach to control the blood glucose level using a nonlinear model predictive control. This method, maintains the level of blood glucose concentration within a normal range. Thus, the blood glucose level is measured in each minute and predicted for the next time interval. If that is not in the normal range, amount of the insulin which must be injected will be determined. The proposed control approach includes important features such as model uncertainties and prevents acute decrease in the blood glucose level, and instability. In order to assess performance of the proposed controller, computer simulations have been carried out in Matlab/Simulink. Simulation results will reveal the effectiveness of the proposed nonlinear model predictive controller in adjusting the blood glucose level by injecting required insulin. So if the nutrition of the person decreases instantly, the hypoglycemia does not happen.
Cite this paper
Ahmadi, M. and Jafari, A. (2012) Is nonlinear model predictive control with fuzzy predictive model proper for managing the blood glucose level in typeI diabetes?. Journal of Biomedical Science and Engineering, 5, 63-74. doi: 10.4236/jbise.2012.52010.
Ahmadi, M. and Jafari, A. (2012) Is nonlinear model predictive control with fuzzy predictive model proper for managing the blood glucose level in typeI diabetes?. Journal of Biomedical Science and Engineering, 5, 63-74. doi: 10.4236/jbise.2012.52010.
References
[1] Ahrens, B. and Taborsky, Jr. (2002) B-cell function and insulin secretion. In: Baron, A., Porte, D. and Sherwin, R.S. Eds., Ellenberg and Rifkin’s Diabetes Mellitus, Mc-Graw-Hill Professional, New York, 43-65. [2] Albisser, A.M. (1982) Insulin delivery systems: Do they need a glucose sensor? Diabetes Care, 3, 166-173. doi:10.2337/diacare.5.3.166 [3] Furler, S.M., Kraegen, E.W., Smallwood, R.H. and Chisolm, A.J. (1985) Blood glucose control by intermittent loop closure in the basal mode: Computer simulation studies with diabetic model. Diabetes Care, 8, 553-561. doi:10.2337/diacare.8.6.553 [4] Lee, C.K., Cho, J.H. and Lee, I.B. (2002) PID control in typeI diabetic patient. Theories and Applications of Chemical Engineering, 3063-3067. [5] Andersen, K.E. and Hejbjerre, M. (2002) A bayesian approach to Bergman minimal model. http://research.microsoft.com/en-us/um/cambridge/events/aistats2003/proceedings/183.pdf [6] Li, J., Kaung, Y. and Mason, C. (2006) Modeling the glucose-insulin regulatory system and ultradian insulin secretory oscillations with two explicit time delays. Journal of Theoretical Biology, 242, 722-735. doi:10.1016/j.jtbi.2006.04.002 [7] Findeisen, R., et al., (2002) An introduction to nonlinear model predictive control. 21st Benelux Meeting on Systems and Control, Veidhoven. [8] Wang, L. (1997) A course in fuzzy systems and control. Prentice-Hall, Inc., Upper Saddle River, 196-226.
[1] Ahrens, B. and Taborsky, Jr. (2002) B-cell function and insulin secretion. In: Baron, A., Porte, D. and Sherwin, R.S. Eds., Ellenberg and Rifkin’s Diabetes Mellitus, Mc-Graw-Hill Professional, New York, 43-65. [2] Albisser, A.M. (1982) Insulin delivery systems: Do they need a glucose sensor? Diabetes Care, 3, 166-173. doi:10.2337/diacare.5.3.166 [3] Furler, S.M., Kraegen, E.W., Smallwood, R.H. and Chisolm, A.J. (1985) Blood glucose control by intermittent loop closure in the basal mode: Computer simulation studies with diabetic model. Diabetes Care, 8, 553-561. doi:10.2337/diacare.8.6.553 [4] Lee, C.K., Cho, J.H. and Lee, I.B. (2002) PID control in typeI diabetic patient. Theories and Applications of Chemical Engineering, 3063-3067. [5] Andersen, K.E. and Hejbjerre, M. (2002) A bayesian approach to Bergman minimal model. http://research.microsoft.com/en-us/um/cambridge/events/aistats2003/proceedings/183.pdf [6] Li, J., Kaung, Y. and Mason, C. (2006) Modeling the glucose-insulin regulatory system and ultradian insulin secretory oscillations with two explicit time delays. Journal of Theoretical Biology, 242, 722-735. doi:10.1016/j.jtbi.2006.04.002 [7] Findeisen, R., et al., (2002) An introduction to nonlinear model predictive control. 21st Benelux Meeting on Systems and Control, Veidhoven. [8] Wang, L. (1997) A course in fuzzy systems and control. Prentice-Hall, Inc., Upper Saddle River, 196-226.