| 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 | Proc IEEE Conf Decis 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 |