| Title | CHoKI-based MPC for blood glucose regulation in Artificial Pancreas |
|---|---|
| Authors | Sonzogni B. , MANZANO CRESPO, JOSÉ MARÍA, Polver M. , Previdi F. , Ferramosca A. |
| External publication | No |
| Means | IFAC PAPERSONLINE |
| Scope | Conference Paper |
| Nature | Científica |
| SJR Impact | 0.365 |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184340008&doi=10.1016%2fj.ifacol.2023.10.276&partnerID=40&md5=7c6b7801720fae6e8cdcad4a1d42c558 |
| Publication date | 01/01/2023 |
| Scopus Id | 2-s2.0-85184340008 |
| DOI | 10.1016/j.ifacol.2023.10.276 |
| Abstract | This work presents a Model Predictive Control (MPC) algorithm for the artificial pancreas able to autonomously manage basal insulin injections in type 1 diabetic patients. The MPC goal is to maintain the blood glucose inside the safe range (70-180 mg/dL) acting on the insulin amount, using a model to make predictions of the system behavior and satisfying operational constraints. The complexity of diabetes complicates the identification of a general physiological model, so a data-driven learning method is proposed, the Componentwise Hölder Kinky Inference (CHoKI), leading to customized controllers. For the data collection phase and also to test the proposed controller, the FDA-accepted UVA/Padova simulator is exploited. The final results are promising since the proposed controller reduces the time in hypoglycemia if compared to the standard constant basal insulin therapy, satisfying also the time in range requirements. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
| Keywords | Artificial Pancreas; learning-based control; MPC |
| Universidad Loyola members |