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Authors
Garcia-Gabin, Winston; Zambrano, Darine; Bondia Company, Jorge; Vehi, Josep

Identifier
10.3182/20090812-3-DK-2006.00015

Index Terms
Control of physiological and clinical variables,; Endocrine and metabolic systems

Abstract
The development of robust and efficient glucose control algorithms is key to making the artificial pancreas a reality. In this paper a sliding mode predictive control (SMPC) is obtained by combining the design technique of a sliding mode control with a model based predictive control (MPC). The SMPC combines the main advantages of the two control methods: the robust features of SMC and the good performance of MPC, including the handling of constraints on manipulated and controlled variables. Control action is composed by three parts: a predictive one from an optimization problem, a discontinuous one given by the switching term and finally, a feed-forward action given by an insulin bolus that is injected when a meal is ingested. The prediction model is linear and it is represented by a second order model with time delay. In order to test the controller in silico experiments, the Hovorka model has been considered. The proposed control algorithm shows considerable robustness for intra-patient variability, as well as an enhanced ability to handle measurement uncertainties and disturbance rejection, especially focusing on postprandial behaviour.

References

Breton, M. and Kovatchev, B. (2008). Analysis, modeling,
and simulation of the accuracy of continuous glucose
sensors. Journal of Diabetes Science and Technology,
2(5), 853–862.

Cordingley, J., Vlasselaers, D., Dormand, N., Wouters,
P., Squire, S., Chassin J., L., Wilinska, M., Morgan,
C., Hovorka, R., and den Berghe, G. (2009). Intensive
insulin therapy: enhanced model predictive control algorithm
versus standard care. Intensive Care Medicine,
25(1), 123 – 128.

Corradini, M.L. and Orlando, G. (1997). A VSC algorithm
based on generalized predictive control. Automatica,
33(5), 927–932.

Dua, P., Doyle, F., and Pistikopoulos, E. (2006). Modelbased
blood glucose control for type 1 diabetes via parametric
programming. IEEE Engineering in Medicine
and Biology Magazine, 53(8), 1478 – 1491.

Garcia-Gabin, W. and Camacho, E.F. (2003). Sliding
mode model based predictive control for non minimum
phase systems. In European Control Conference. Cambridge,
UK.

Garcia-Gabin, W., Dorado, F., and Bordons, C. (2007).
Comparative analysis of sliding mode and Volterra
model predictive controllers applied to a benchmark
CSTR non-minimum phase reactor. In European Control
Conference. Kos, Greece.

Garcia-Gabin, W., Zambrano, D., and Camacho, E.F.
(2005). Sliding mode predictive control for chemical
process with time delay. In 16TH IFAC World Congress.
Prague, Czech Republic.

Garcia-Gabin, W., Zambrano, D., and Camacho, E.F.
(2009). Sliding mode predictive control of a solar air
conditioning plant. Control Engineering Practice, 17(6),
652–663.

Gillis, R., Palerm, C., Zisser, H., Jovanovi, L., Seborg,
D., and Doyle., F.J. (2007). Glucose estimation and
prediction through meal responses using ambulatory
subject data for advisory mode model predictive control.
Journal of Diabetes Science and Technology, 6(1), 825 –
833.

Hovorka, R., Canonico, V., Chassin, L., Haueter, U.,
Massi-Benedetti, M., Orsini Federici, M., Pieber, T.,
Schaller, H., Schaupp, L., Vering, T., and Wilinska, M.
(2004). Nonlinear model predictive control of glucose
concentration in subjects with type 1 diabetes. Physiological
Measurement, 4(4), 905–920.

Hovorka, R., Shojaee-Moradie, F., Carroll, P., Chassin,
L., Gowrie, I., Jackson, N., Tudor, R., Umpleby, A.,
and Jones, R. (2002). Partitioning glucose distribution/
transport, disposal, and endogenous production
during ivgtt. American Journal of Physiology, Endocrinology
and Metabolism, 282(5), 992–1007.

Scheiner, G. and Boyer, B. (2005). Characteristics of
basal insulin requirements by age and gender in type-1
diabetes patients using insulin pump therapy. Diabetes
Research and Clinical Practice, 69(1), 14–21.

Schlotthauer, G., Gamero, L.and Torres, M., and Nicolini,
G. (2005). Modeling, identification and nonlinear model
predictive control of type i diabetic patient. Medical
Engineering and Physics, 28(3), 240 – 250.

Steil, G., Rebrin, K., Darwin, C., Hariri, F., and Saad, M.
(2006). Feasibility of automating insulin delivery for the
treatment of type 1 diabetes. Diabetes, 55, 3344–3350.
Utkin, V.I. (1981). Sliding modes in control and optimization.
Spring-Verlag, Moscow.

Yao, K., Gao, F., and Allgower, F. (2008). Barrel temperature
control during operation transition in injection
molding. Control Engineering Practice, 16(11), 1259–
1264.

Zhou, J., Liu, Z., and Pei, R. (2001). A new non linear
model predictive control scheme for discrete time
systems based on sliding mode control. In American
Control Conference, 3079–3084. Arlington.

Zinober, A. (1994). Variable Structure and Lyapunov
Control. Spring-Verlag, London.