Maximizing performance of linear model predictive control of glycemia for T1DM subjects
The primary objective of this paper is the custom design of an effective, yet relatively easyto- implement, predictive control algorithm to maintain normoglycemia in patients with type 1 diabetes. The proposed patient-tailorable empirical model featuring the separated feedback dynamics to model the...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2022-06-01
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| Series: | Archives of Control Sciences |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/123555/PDF/art04_int.pdf |
| _version_ | 1817970699763974144 |
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| author | Martin Dodek Eva Miklovicová |
| author_facet | Martin Dodek Eva Miklovicová |
| author_sort | Martin Dodek |
| collection | DOAJ |
| description | The primary objective of this paper is the custom design of an effective, yet relatively easyto- implement, predictive control algorithm to maintain normoglycemia in patients with type 1 diabetes. The proposed patient-tailorable empirical model featuring the separated feedback dynamics to model the effect of insulin administration and carbohydrate intake was proven to be suitable for the synthesis of a high-performance predictive control algorithm for artificial pancreas.Within the introduced linear model predictive control law, the constraints were applied to the manipulated variable in order to reflect the technical limitations of insulin pumps and the typical nonnegative nature of the insulin administration. Similarly, inequalities constraints for the controlled variable were also assumed while anticipating suppression of hypoglycemia states during the automated insulin treatment. However, the problem of control infeasibility has emerged, especially if one uses too tight constraints of the manipulated and the controlled variable concurrently. To this end, exploiting the Farkas lemma, it was possible to formulate the helper linear programming problem based on the solution of which this infeasibility could be identified and the optimality of the control could be restored by adapting the constraints. This adaptation of constraints is asymmetrical, thus one can force to fully avoid hypoglycemia at the expense of mild hyperglycemia. Finally, a series of comprehensive in-silico experiments were carried out to validate the presented control algorithm and the proposed improvements. These simulations also addressed the control robustness in terms of the intersubject variability and the meal announcements uncertainty. |
| first_indexed | 2024-04-13T20:37:26Z |
| format | Article |
| id | doaj.art-cd86615aa9ed40e599b4c2f77419e5a8 |
| institution | Directory Open Access Journal |
| issn | 1230-2384 |
| language | English |
| last_indexed | 2024-04-13T20:37:26Z |
| publishDate | 2022-06-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Control Sciences |
| spelling | doaj.art-cd86615aa9ed40e599b4c2f77419e5a82022-12-22T02:30:59ZengPolish Academy of SciencesArchives of Control Sciences1230-23842022-06-01vol. 32No 2305333https://doi.org/10.24425/acs.2022.141714Maximizing performance of linear model predictive control of glycemia for T1DM subjectsMartin Dodek0Eva Miklovicová1Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, SlovakiaInstitute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, SlovakiaThe primary objective of this paper is the custom design of an effective, yet relatively easyto- implement, predictive control algorithm to maintain normoglycemia in patients with type 1 diabetes. The proposed patient-tailorable empirical model featuring the separated feedback dynamics to model the effect of insulin administration and carbohydrate intake was proven to be suitable for the synthesis of a high-performance predictive control algorithm for artificial pancreas.Within the introduced linear model predictive control law, the constraints were applied to the manipulated variable in order to reflect the technical limitations of insulin pumps and the typical nonnegative nature of the insulin administration. Similarly, inequalities constraints for the controlled variable were also assumed while anticipating suppression of hypoglycemia states during the automated insulin treatment. However, the problem of control infeasibility has emerged, especially if one uses too tight constraints of the manipulated and the controlled variable concurrently. To this end, exploiting the Farkas lemma, it was possible to formulate the helper linear programming problem based on the solution of which this infeasibility could be identified and the optimality of the control could be restored by adapting the constraints. This adaptation of constraints is asymmetrical, thus one can force to fully avoid hypoglycemia at the expense of mild hyperglycemia. Finally, a series of comprehensive in-silico experiments were carried out to validate the presented control algorithm and the proposed improvements. These simulations also addressed the control robustness in terms of the intersubject variability and the meal announcements uncertainty.https://journals.pan.pl/Content/123555/PDF/art04_int.pdfdiabetes mellitusartificial pancreasglycemia controlpredictive controlconstrained optimizationcontrol feasibility |
| spellingShingle | Martin Dodek Eva Miklovicová Maximizing performance of linear model predictive control of glycemia for T1DM subjects Archives of Control Sciences diabetes mellitus artificial pancreas glycemia control predictive control constrained optimization control feasibility |
| title | Maximizing performance of linear model predictive control of glycemia for T1DM subjects |
| title_full | Maximizing performance of linear model predictive control of glycemia for T1DM subjects |
| title_fullStr | Maximizing performance of linear model predictive control of glycemia for T1DM subjects |
| title_full_unstemmed | Maximizing performance of linear model predictive control of glycemia for T1DM subjects |
| title_short | Maximizing performance of linear model predictive control of glycemia for T1DM subjects |
| title_sort | maximizing performance of linear model predictive control of glycemia for t1dm subjects |
| topic | diabetes mellitus artificial pancreas glycemia control predictive control constrained optimization control feasibility |
| url | https://journals.pan.pl/Content/123555/PDF/art04_int.pdf |
| work_keys_str_mv | AT martindodek maximizingperformanceoflinearmodelpredictivecontrolofglycemiafort1dmsubjects AT evamiklovicova maximizingperformanceoflinearmodelpredictivecontrolofglycemiafort1dmsubjects |