A comprehensive review of models and nonlinear control strategies for blood glucose regulation in artificial pancreas

Mughal, Iqra Shafeeq; Patanè, Luca; Caponetto, Riccardo

doi:10.1016/j.arcontrol.2024.100937

Annual Reviews in Control

2024

DOI: 10.1016/j.arcontrol.2024.100937

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A comprehensive review of models and nonlinear control strategies for blood glucose regulation in artificial pancreas

Iqra Shafeeq Mughal,

Luca Patanè,

Riccardo Caponetto

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2024

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Cited by 3 publications

References 85 publications

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Analysis of the performances of various controllers adopted in the biomedical field for blood glucose regulation: a case study of the type-1 diabetes

Abubakar,

Essabbar,

Saikouk

2023

Journal of Medical Engineering & Technology

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Analysis of the performances of various controllers adopted in the biomedical field for blood glucose regulation: a case study of the type-1 diabetes

Abubakar,

Essabbar,

Saikouk

2023

Journal of Medical Engineering & Technology

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Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes

Dénes-Fazakas,

Szilágyi,

Kovács

et al. 2024

Biomedicines

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Background/Objectives: Managing blood glucose levels effectively remains a significant challenge for individuals with diabetes. Traditional methods often lack the flexibility needed for personalized care. This study explores the potential of reinforcement learning-based approaches, which mimic human learning and adapt strategies through ongoing interactions, in creating dynamic and personalized blood glucose management plans. Methods: We developed a mathematical model specifically for patients with type IVP diabetes, validated with data from 10 patients and 17 key parameters. The model includes continuous glucose monitoring (CGM) noise and random carbohydrate intake to simulate real-life conditions. A closed-loop system was designed to enable the application of reinforcement learning algorithms. Results: By implementing a Policy Optimization (PPO) branch, we achieved an average Time in Range (TIR) metric of 73%, indicating improved blood glucose control. Conclusions: This study presents a personalized insulin therapy solution using reinforcement learning. Our closed-loop model offers a promising approach for improving blood glucose regulation, with potential applications in personalized diabetes management.

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Dynamic analysis of a Filippov blood glucose insulin model

Wu,

Yu,

et al. 2024

MATH

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<abstract><p>This paper proposed a Filippov blood glucose insulin model with threshold control strategy and studied its dynamic properties. Using Filippov's convex method, we proved the global stability of its two subsystems, the existence and conditions of the sliding region of the system were also given, and different types of equilibrium states of the system were also addressed. The existence and stability of pseudo equilibrium points were thoroughly discussed. Through numerical simulations, we have demonstrated that it is possible to effectively control blood sugar concentrations to achieve more cost-effective treatment levels by selecting an appropriate threshold range for insulin injection.</p></abstract>

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A comprehensive review of models and nonlinear control strategies for blood glucose regulation in artificial pancreas

Cited by 3 publications

References 85 publications

Analysis of the performances of various controllers adopted in the biomedical field for blood glucose regulation: a case study of the type-1 diabetes

Analysis of the performances of various controllers adopted in the biomedical field for blood glucose regulation: a case study of the type-1 diabetes

Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes

Dynamic analysis of a Filippov blood glucose insulin model

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