Autors: Kralev, J. K., Markovski, A. G., Slavov, T. N., Georgieva P.
Title: Uncertain Model Identification in Type i Diabetes: A Robust Control Framework for Glucose Regulation
Keywords: glucose regulation, Hovorka model, robust control, Type 1 Diabetes, uncertain model identification

Abstract: This paper addresses the challenges of glucose regulation in Type 1 Diabetes (T1D) by developing an uncertain model identification framework. The approach utilizes small random variations in insulin delivery rates to identify a robust control model. The Hovorka model is adapted to incorporate uncertainty, leading to a linear time-invariant (LTI) model with multiplicative uncertainty. A robust controller is designed to ensure stability and performance under model uncertainty. The controller effectiveness is validated using the FDA-approved UVa/Padova simulator, demonstrating successful glucose regulation within the target range.

References

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Issue

2025 14th Mediterranean Conference on Embedded Computing, MECO 2025 - Proceedings, 2025, Montenegro, https://doi.org/10.1109/MECO66322.2025.11049269

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science