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.

(2024). CHoKI-Based MPC for Blood Glucose Regulation in Artificial Pancreas with Probabilistic Constraints . Retrieved from https://hdl.handle.net/10446/263055

CHoKI-Based MPC for Blood Glucose Regulation in Artificial Pancreas with Probabilistic Constraints

Sonzogni, Beatrice;Polver, Marco;Previdi, Fabio;Ferramosca, Antonio
2024-12-01

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.
dic-2024
Sonzogni, Beatrice; Manzano, José María; Polver, Marco; Previdi, Fabio; Ferramosca, Antonio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/263055
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