Model predictive control of blood sugar in patients with type-1 diabetes

In this article, two adaptive model predictive controllers (AMPC) are applied to regulate the blood glucose in type 1 diabetic patients. The first controller is constructed based on a linear model, while the second one is designed by using a nonlinear Hammerstein model. The adaptive version of these control schemes is considered to make them more robust against model mismatches and external disturbances. The least squares method with forgetting factor is used to update the model parameters. For simulation study, two well-known mathematical models namely, Puckett and Hovorka which describe the dynamical behavior of patient's body have been selected. The performances and robustness of the proposed controllers are tested for regulating the blood glucose of diabetic patients in presences of model mismatches and measurement noises. Simulation results indicate that the non-linear model predictive controller (NMPC) outperforms the linear one. To improve the performance of the NMPC in rejecting the meal disturbances, two different feedforward control strategies have been considered. Simulation results indicate that the combined adaptive NMPC with feedforward controller has a better performance over the other considered control schemes. Copyright (c) 2015 John Wiley & Sons, Ltd.