[1] H. C. Schaller, L. Schaupp, M. Bodenlenz, M. E. Wilinska, L. J. Chassin, P. Wach, T. Vering, R. Hovarka, T. R. Pieber, “On-line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with type 1 diabetes” Diabetic Medicin, Vol 23, No 1, pp 90-93, 2006.
[2] B. Candas, J. Radziuk, “An adaptive plasma glucose controller based on a nonlinear insulin/glucose model” IEEE Trans. Biomed. Eng. Vol 41, No 2, pp 116-124, 1994.
[3] D.
S. Patek, D. Marc Breton, Y. Chen, C. Solomon, B. Kovatchev, “Linear quadratic Gaussian-based closed-loop control of type 1 diabetes”
J Diabetes Sci Technol Vol 1, No 6, pp 834–841, 2007.
[4] Y. Irma Sánchez Chávez, O. Sergio Martínez Chapa, R. Morales-Menéndez, “Glucose optimal control system in diabetes treatment, Applied Mathematics and Computation” Vol 209, No 1, pp 19-30, 2009.
[5] M. Eren-Oruklu, A. Cinar, “Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes” Journal of process control Vol 19, No 8, pp 1333-1346, 2009.
[6] R. Hovorka, V.
Canonico, L. J.
Chassin, U.
Haueter, et al. “Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes”
Physiol. Meas Vol 25, No 4, pp 905–920, 2004.
[7] Y. Wang, F. Doyle, “Closed-loop control of artificial pancreatic ß-cell in type 1 diabetes mellitus using model predictive iterative learning control” IEEE Transactions on Biomedical Engineering Vol 57, No 2, pp 211-219, 2010.
[8] K. Lunze, T. Singh, M. Walter, M. Brendel, S. Leonhardt, “Blood glucose control algorithms for type 1 diabetic patients: A methodological review” Biomedical Signal Processing and Control Vol 8, No 2, pp 107– 119, 2013.
[9] E. Ruiz-Velázquez, R. Femat, D. U. Campos-Delgado, “Blood glucose control for type I diabetes mellitus: A robust tracking H∞ problem” Control engineering practice, Vol 12, No 9, pp 1179-1195, 2004.
[10] F. Chee, A. V.
Savkin, T. L.
Fernando, S.
Nahavandi, “Optimal H
∞ insulin injection control for blood glucose regulation in diabetic patients,
IEEE Transactions on Biomedical Engineering, Vol 52 , No 10, pp 162-1631, 2005.
[11] J. T. Sorensen, “A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes” Massachusetts Institute of Technology 1985.
[12] R. N. Bergman, L .S .Phillips, C. Cobelli, “Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and b-cell glucose sensitivity from the response to intravenous glucose” J. Clin. Invest Vol 68, pp 1456–1467, 1981.
[13] C. Dalla Man, R. Rizza, C. Cobelli, “Meal Simulation Model of the Glucose-Insulin System” IEEE Transaction on biomedical engineering Vol 54, No 10, 2007.
[14] S. Karra, M. N. Karim, B. Han, “Predictive control of blood glucose concentratio in type-I diabetic patients using linear input–output models” In Proc. 10th Int. IFAC Symp. Comp. Appl. Biotech., Cancun, Mexico, Vol 1, pp 147-152, 2007.
[15] X. W. Wong, J. G. Chase, G. M. Shaw, C. E. Hann, T. Lotz, J. Lin, et al. “Model predictive glycemic regulation in critical illness using insulin and nutrition input: a pilot study” Med. Eng. Phys. Vol 28, pp 665–681, 2006.
[16] P. Soru, G. De Nicolao, C. Toffanin, C. Dalla Man, C. obelli, L. Magni,” MPC based Artificial Pancreas: Strategies for individualization and meal compensation” Annual Reviews in Control Vol 36, No 1, pp 118–128, 2012.
[17] G. M. Steil, K. Rebrin, C .Darwin, F. Hariri, M. F. Saad, “Feasibility of automating insulin delivery for the treatment of type 1 diabetes” Diabetes Vol 55, No 12, pp 3344-50, 2006.
[18] L. Magni, D. M. Raimondo, C. DallaMan, G. DeNicolao, B. Kovatchev, C. Cobelli, “Model predictive control of glucose concentration in type 1 diabetes patients” An in silico trialBiomedical Signal processing and control, Vol 4 No 4, pp 338-346, 2009.
[19] D. Finan, C. Palerm, F. Doyle, et al. “Effect of input excitation on the quality of empirical dynamic models for type 1 diabetes” AIChE Journal Vol 55, pp 1135–1146, 2009.
[20] C. Dalla Man, D. M.
Raimondo, R. A.
Rizza, C.
Cobelli, “GIM, Simulation Software of Meal Glucose–Insulin Model”
Journal of Diabetes Science and Technolog, Vol 1, No 3, pp 323-330, 2007.
[21] H. Huang, “A modified smith predictor with an approximate inverse of dead time” AiChE Journal Vol 36, No 7, pp 1025-1031, 1990.
[22] C. Palerm, et al. “Prandial insulin dosing using run-to-run control: application of clinical data and medical expertise to define a suitable performance metric” Diabetes Care, Vol.30, No.5, pp 1131-1136, 2007.
[23] B. Kovatchev, E. Otto, D. Cox, L. Gonder-Frederick, W. Clarke, “Evaluation of a new measure of blood glucose variability in diabetes” Diabetes Care Vol 29, No 11, pp 2433–2438, 2006.