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

doi:10.1002/rnc.6030

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 journal › Article › peer-review

Abstract

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 language	English
Pages (from-to)	5087-5104
Number of pages	18
Journal	International Journal of Robust and Nonlinear Control
Volume	33
Issue number	9
DOIs	https://doi.org/10.1002/rnc.6030
State	Published - Jun 2023

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:
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, 296692This 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.

Keywords

Diabetes Mellitus
adaptive observer
metabolic systems
sliding-modes

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10.1002/rnc.6030

Cite this

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

abstract = "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.",

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AU - Franco, Roberto

AU - Ríos, Héctor

AU - Ferreira de Loza, Alejandra

AU - Cassany, Louis

AU - Gucik-Derigny, David

AU - Cieslak, Jérôme

AU - Henry, David

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AB - 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.

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

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Keywords

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