Parameter identification and state estimation for a diabetic glucose-insulin model via an adaptive observer

Roberto Franco, Héctor Ríos, Alejandra Ferreira de Loza, Louis Cassany, David Gucik-Derigny, Jérôme Cieslak, David Henry

Research output: Contribution to journalArticlepeer-review


In this article, an adaptive observer is designed for patients with Type 1 Diabetes Mellitus. The adaptive observer, synthesized using the so-called Bergman's Minimal Model, simultaneously estimates the states and the parameter corresponding to the insulin-independent glucose disappearance rate. The adaptive observer deals with parameter uncertainties, whereas the food intake is regarded as an external disturbance. The adaptive observer relies on intravenous glucose measurements. The state estimation error converges to a neighborhood of the origin despite the effects of the external disturbances and uncertainties, while the parameter estimation error converges in a fixed time to a neighborhood of the origin. The adaptive observer synthesis is given by a constructive method based on linear matrix inequalities. Simulation results show the feasibility of the proposed scheme. Moreover, the approach is validated in UVA/Padova metabolic simulator for ten in silico adult patients.

Original languageEnglish
JournalInternational Journal of Robust and Nonlinear Control
StateAccepted/In press - 2022

Bibliographical note

Funding Information:
This work was supported in part by the ECOS Nord Project under Grant M18M01, in part by SEP‐CONACYT‐ECOS‐ANUIES under Grant 296692, and in part by the French National Agency for Research under Grant DIABLO ANR‐18‐CE17‐0005‐01. The work of Roberto Franco and Héctor Ríos was supported in part by CONACYT CVU 772057, in part by Cátedras CONACYT CVU 270504 Project 922, and in part by TecNM Projects. The work of Alejandra Ferreira de Loza was supported by Cátedras CONACYT CVU 166403 Project 1537.

Funding Information:
Cátedras CONACYT, 166403 Project 1537; 270504 Project 922; Consejo Nacional de Ciencia y Tecnología, 772057; ECOS Nord Project, M18M01; French National Agency for Research, DIABLO ANR‐18‐ CE17‐0005‐01; SEP‐CONACYT‐ECOS‐ANUIES, 296692 Funding information

Publisher Copyright:
© 2022 John Wiley & Sons Ltd.


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