“…The effectiveness of the model is assessed by contrasting it with an earlier control-oriented model, and the study shows that it performs better in terms of open-loop and closed-loop differences compared to a popular metabolic simulator. The suggested model has the potential to increase the glycemia regulation controllers' reliability and robustness in T1DM [6].…”
The development of an artificial pancreas (AP) has been a topic of interest in the field of diabetes management for several decades. An AP system is designed to mimic the function of the pancreas by continuously monitoring blood glucose levels and delivering insulin or glucagon in response to changes in glucose concentration. Mathematical models play a crucial role in the development and evaluation of AP systems, as they enable the simulation and prediction of the system’s performance. This review paper provides an overview of the mathematical models used in AP research. The paper discusses the strengths and limitations of each type of model, as well as their applications in AP research. The review also highlights the challenges and opportunities in AP model development, such as the need for personalized models and the integration of data from multiple sources. Overall, this review provides a comprehensive understanding of the role of mathematical models in AP research and their potential for improving diabetes management.
“…The effectiveness of the model is assessed by contrasting it with an earlier control-oriented model, and the study shows that it performs better in terms of open-loop and closed-loop differences compared to a popular metabolic simulator. The suggested model has the potential to increase the glycemia regulation controllers' reliability and robustness in T1DM [6].…”
The development of an artificial pancreas (AP) has been a topic of interest in the field of diabetes management for several decades. An AP system is designed to mimic the function of the pancreas by continuously monitoring blood glucose levels and delivering insulin or glucagon in response to changes in glucose concentration. Mathematical models play a crucial role in the development and evaluation of AP systems, as they enable the simulation and prediction of the system’s performance. This review paper provides an overview of the mathematical models used in AP research. The paper discusses the strengths and limitations of each type of model, as well as their applications in AP research. The review also highlights the challenges and opportunities in AP model development, such as the need for personalized models and the integration of data from multiple sources. Overall, this review provides a comprehensive understanding of the role of mathematical models in AP research and their potential for improving diabetes management.
“…Other models have also been used for glucose‐insulin control; for example, in reference 20, a pharmacokinetic‐pharmacodynamic model is used to study the effects of stress, meals, and sleep deprivation under the effect of delayed states. In reference 21, a model‐based control of a hybrid AP capable of operating with unknown meal and exercise disturbances is presented. In reference 22, a compartmental model is used to control blood glucose levels for T1DM patients, applying a controller.…”
This article introduces an observer‐based control strategy tailored for regulating plasma glucose in type 1 diabetes mellitus patients, addressing challenges like unknown time‐varying delays and meal disturbances. This control strategy is based on an extended Bergman minimal model, a nonlinear glucose‐insulin model to encompass unknown inputs, such as unplanned meals, exercise disturbances, or delays. The primary contribution lies in the design of an observer‐based state feedback control in the presence of unknown long delays, which seeks to support and enhance the performance of the traditional artificial pancreas by considering realistic scenarios. The observer and control gains for the observer‐based control are computed through linear matrix inequalities formulated from Lyapunov conditions that guarantee closed‐loop stability. This design deploys a soft and gentle dynamic response, similar to a natural pancreas, despite meal disturbances and input delays. Numerical tests demonstrate the scheme's effectiveness in glycemic level regulation and hypoglycemic episode avoidance.
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