Título CHoKI-Based MPC for Blood Glucose Regulation in Artificial Pancreas with Probabilistic Constraints
Autores Sonzogni B. , MANZANO CRESPO, JOSÉ MARÍA, Polver M. , Previdi F. , Ferramosca A.
Publicación externa No
Medio Ieee Conference On Decision And Control
Alcance Conference Paper
Naturaleza Científica
Impacto SJR 0.721
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184811896&doi=10.1109%2fCDC49753.2023.10383910&partnerID=40&md5=3299370898a86b38fa442df4826897df
Fecha de publicacion 01/01/2023
Scopus Id 2-s2.0-85184811896
DOI 10.1109/CDC49753.2023.10383910
Abstract 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.
Miembros de la Universidad Loyola

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