Data-Driven Robust Control for Type 1 Diabetes 231- Laxminarayan, S., Reifman, J., Steil, G.M.: Use of a food and drug administration-
approved type 1 diabetes mellitus simulator to evaluate and optimize a
proportional-integral-derivative controller. J. Diab. Sci. Technol. 6 (6), 1401–1412
(2012) - Lee, H., Buckingham, B.A., Wilson, D.M., Bequette, B.W.: A closed-loop artificial
pancreas using model predictive control and a sliding meal size estimator. J. Diab.
Sci. Technol. 3 (5), 1082–1090 (2009) - Lee, J.J., Gondhalekar, R., Doyle, F.J.: Design of an artificial pancreas using zone
model predictive control with a moving horizon state estimator. In: 2014 IEEE
53rd Annual Conference on Decision and Control (CDC), pp. 6975–6980. IEEE
(2014) - Lenart, P.J., Parker, R.S.: Modeling exercise effects in type i diabetic patients.
IFAC Proc. Vols. 35 (1), 247–252 (2002) - Ly, T.T., et al.: Day and night closed-loop control using the integrated Medtronic
hybrid closed-loop system in type 1 diabetes at diabetes camp. Diab. Care 38 (7),
1205–1211 (2015) - Magni, L., et al.: Model predictive control of glucose concentration in type I dia-
betic patients: an in silico trial. Biomed. Signal Process. Control 4 (4), 338–346
(2009) - Paoletti, N., Liu, K.S., Smolka, S.A., Lin, S.: Data-driven robust control for type
1 diabetes under meal and exercise uncertainties. CoRR, 1707.02246 (2017) - Parker, R.S., Doyle, F.J., Ward, J.H., Peppas, N.A.: Robust H∞glucose control
in diabetes using a physiological model. AIChE J. 46 (12), 2537–2549 (2000) - Perea, L., How, J., Breger, L., Elosegui, P.: Nonlinearity in sensor fusion: divergence
issues in EKF, modified truncated GSF, and UKF. In: AIAA Guidance, Navigation
and Control Conference and Exhibit, p. 6514 (2007) - Rao, C.V., Rawlings, J.B., Mayne, D.Q.: Constrained state estimation for nonlinear
discrete-time systems: stability and moving horizon approximations. IEEE Trans.
Autom. Control 48 (2), 246–258 (2003) - Resalat, N., El Youssef, J., Reddy, R., Jacobs, P.G.: Design of a dual-hormone
model predictive control for artificial pancreas with exercise model. In: 2016 IEEE
38th Annual International Conference of the Engineering in Medicine and Biology
Society (EMBC), pp. 2270–2273. IEEE (2016) - Ruiz-Vel ́azquez, E., Femat, R., Campos-Delgado, D.: Blood glucose control for
type I diabetes mellitus: a robust tracking H∞problem. Control Eng. Pract. 12 (9),
1179–1195 (2004) - Stoorvogel, A.A.: The H∞Control Problem: A State Space Approach. Prentice
Hall, Upper Saddle River (1992) - Szalay, P., Eigner, G., Kov ́acs, L.A.: Linear matrix inequality-based robust con-
troller design for type-1 diabetes model. IFAC Proc. Vols. 47 (3), 9247–9252 (2014) - Van Der Merwe, R.: Sigma-point Kalman filters for probabilistic inference in
dynamic state-space models. Ph.D. thesis, Oregon Health & Science University
(2004) - Wang, Y., Zisser, H., Dassau, E., Jovanoviˇc, L., Doyle, F.J.: Model predictive con-
trol with learning-type set-point: Application to artificial pancreaticβ-cell. AIChE
J. 56 (6), 1510–1518 (2010) - Weimer, J., Chen, S., Peleckis, A., Rickels, M.R., Lee, I.: Physiology-invariant meal
detection for type 1 diabetes. Diab. Technol. Ther. 18 (10), 616–624 (2016) - Welch, G., Bishop, G.: An Introduction to the Kalman Filter. Technical report,
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (1995)