Modelling and optimal control of blood glucose levels in the human body
Regulating the blood glucose level is a challenging control problem for the human body. Abnormal blood glucose levels can cause serious health problems over time, including diabetes. Although several mathematical models have been proposed to describe the dynamics of glucose-insulin interaction, none...
| Main Authors: | , , |
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| Format: | Journal Article |
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American Institute of Mathematical Sciences
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/18325 |
| _version_ | 1848749712332554240 |
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| author | Al Helal, Z. Rehbock, Volker Loxton, Ryan |
| author_facet | Al Helal, Z. Rehbock, Volker Loxton, Ryan |
| author_sort | Al Helal, Z. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Regulating the blood glucose level is a challenging control problem for the human body. Abnormal blood glucose levels can cause serious health problems over time, including diabetes. Although several mathematical models have been proposed to describe the dynamics of glucose-insulin interaction, none of them have been universally adopted by the research community. In this paper, we consider a dynamic model of the blood glucose regulatory system originally proposed by Liu and Tang in 2008. This model consists of eight state variables naturally divided into three subsystems: the glucagon and insulin transition subsystem, the receptor binding subsystem and the glucosesubsystem. The model contains 36 model parameters, many of which are unknown and difficult to determine accurately. We formulate an optimal parameter selection problem in which optimal values for the model parameters must be selected so that the resulting model best its given experimental data.We demonstrate that this optimal parameter selection problem can be solved readily using the optimal control software MISER 3.3. Using this approach, significant improvements can be made in matching the model to the experimental data. We also investigate the sensitivity of the resulting optimizedmodel with respect to the insulin release rate. Finally, we use MISER 3.3 to determine optimal open loop controls for the optimized model. |
| first_indexed | 2025-11-14T07:25:18Z |
| format | Journal Article |
| id | curtin-20.500.11937-18325 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:25:18Z |
| publishDate | 2015 |
| publisher | American Institute of Mathematical Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-183252019-02-19T05:35:12Z Modelling and optimal control of blood glucose levels in the human body Al Helal, Z. Rehbock, Volker Loxton, Ryan Regulating the blood glucose level is a challenging control problem for the human body. Abnormal blood glucose levels can cause serious health problems over time, including diabetes. Although several mathematical models have been proposed to describe the dynamics of glucose-insulin interaction, none of them have been universally adopted by the research community. In this paper, we consider a dynamic model of the blood glucose regulatory system originally proposed by Liu and Tang in 2008. This model consists of eight state variables naturally divided into three subsystems: the glucagon and insulin transition subsystem, the receptor binding subsystem and the glucosesubsystem. The model contains 36 model parameters, many of which are unknown and difficult to determine accurately. We formulate an optimal parameter selection problem in which optimal values for the model parameters must be selected so that the resulting model best its given experimental data.We demonstrate that this optimal parameter selection problem can be solved readily using the optimal control software MISER 3.3. Using this approach, significant improvements can be made in matching the model to the experimental data. We also investigate the sensitivity of the resulting optimizedmodel with respect to the insulin release rate. Finally, we use MISER 3.3 to determine optimal open loop controls for the optimized model. 2015 Journal Article http://hdl.handle.net/20.500.11937/18325 10.3934/jimo.2015.11.1149 American Institute of Mathematical Sciences fulltext |
| spellingShingle | Al Helal, Z. Rehbock, Volker Loxton, Ryan Modelling and optimal control of blood glucose levels in the human body |
| title | Modelling and optimal control of blood glucose levels in the human body |
| title_full | Modelling and optimal control of blood glucose levels in the human body |
| title_fullStr | Modelling and optimal control of blood glucose levels in the human body |
| title_full_unstemmed | Modelling and optimal control of blood glucose levels in the human body |
| title_short | Modelling and optimal control of blood glucose levels in the human body |
| title_sort | modelling and optimal control of blood glucose levels in the human body |
| url | http://hdl.handle.net/20.500.11937/18325 |