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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2512.05827 |
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| _version_ | 1866915656877735936 |
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| author | Naik, Vihangkumar V. Manzoni, Eleonora Escorihuela-Altaba, Clara Garcia-Tirado, Jose |
| author_facet | Naik, Vihangkumar V. Manzoni, Eleonora Escorihuela-Altaba, Clara Garcia-Tirado, Jose |
| contents | Background and objective: Hybrid automated insulin delivery (hAID) systems represent the most advanced therapy for type 1 diabetes (T1D). Current systems rely on linear or linearized models of glucose homeostasis, which may compromise prediction accuracy and, in turn, timely decision-making by the controller. Physiological variability further complicates insulin requirements, underscoring the need for controllers that adapt dynamically and reduce user burden. Methods: We introduce the University of Bern (UniBE) hAID system, a framework based on successive linearization model predictive control (MPC). The controller integrates basal insulin infusion with the insulin bolus delivery module for meal-related and corrective bolus dosing, adapting bounds in real time to glucose dynamics while accounting for both automated and user-initiated inputs. In-silico evaluation was conducted using the commercial version of the FDA-accepted UVa/Padova metabolic simulator across nine scenarios involving persistent and time-varying errors in meal timing, carbohydrate estimation, and basal insulin profiles. Results: In the baseline scenario, UniBE achieved a mean time in range of 92.0+-13.2%, with time below range at 0.1+-0.2% and time above range at 7.9+-13.2%. Across perturbation scenarios, time in range remained between 75.1 and 92.8%, with low hypoglycemia incidence, demonstrating resilience to clinically relevant disturbances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05827 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advanced Hybrid Automated Insulin Delivery System based on Successive Linearization Model Predictive Control: The UniBE System Naik, Vihangkumar V. Manzoni, Eleonora Escorihuela-Altaba, Clara Garcia-Tirado, Jose Systems and Control Background and objective: Hybrid automated insulin delivery (hAID) systems represent the most advanced therapy for type 1 diabetes (T1D). Current systems rely on linear or linearized models of glucose homeostasis, which may compromise prediction accuracy and, in turn, timely decision-making by the controller. Physiological variability further complicates insulin requirements, underscoring the need for controllers that adapt dynamically and reduce user burden. Methods: We introduce the University of Bern (UniBE) hAID system, a framework based on successive linearization model predictive control (MPC). The controller integrates basal insulin infusion with the insulin bolus delivery module for meal-related and corrective bolus dosing, adapting bounds in real time to glucose dynamics while accounting for both automated and user-initiated inputs. In-silico evaluation was conducted using the commercial version of the FDA-accepted UVa/Padova metabolic simulator across nine scenarios involving persistent and time-varying errors in meal timing, carbohydrate estimation, and basal insulin profiles. Results: In the baseline scenario, UniBE achieved a mean time in range of 92.0+-13.2%, with time below range at 0.1+-0.2% and time above range at 7.9+-13.2%. Across perturbation scenarios, time in range remained between 75.1 and 92.8%, with low hypoglycemia incidence, demonstrating resilience to clinically relevant disturbances. |
| title | Advanced Hybrid Automated Insulin Delivery System based on Successive Linearization Model Predictive Control: The UniBE System |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.05827 |