Sonzogni B. , MANZANO CRESPO, JOSÉ MARÍA, Polver M. , Previdi F. , Ferramosca A.
No
Proc IEEE Conf Decis Control
Conference Paper
Científica
0.721
01/01/2023
2-s2.0-85184811896
This work presents a Model Predictive Control (MPC) algorithm for the Artificial Pancreas. In this work, we assume that an a-priori model is unknown and the Componentwise Hölder Kinky Inference (CHoKI) data-based learning method is used to make glucose predictions. A stochastic formulation of the MPC with chance constraints is considered to have a less conservative controller. The data collection and the testing of the proposed controller are performed by exploiting the virtual patients of the FDA-accepted UVA/Padova simulator. The simulation results are quite satisfying since the time in hypoglycemia is reduced. © 2023 IEEE.