A sliding mode predictive control approach to closed-loop glucose control for type 1 diabetes*

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.