Artificial intelligent pancreas for type 1 diabetic patients using adaptive type 3 fuzzy fault tolerant predictive control
In this paper, the design methodology of artificial intelligent pancreas is presented. Accurate regulation of blood glucose levels in type 1 diabetic patients is of great importance in the presence of possible faults caused by sensor measurements. Regulation of blood glucose levels using a type 3 fu...
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Published in: | Engineering applications of artificial intelligence Vol. 139; p. 109627 |
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Main Authors: | , , , |
Format: | Journal Article |
Language: | English |
Published: |
Elsevier Ltd
01-01-2025
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Subjects: | |
Online Access: | Get full text |
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Summary: | In this paper, the design methodology of artificial intelligent pancreas is presented. Accurate regulation of blood glucose levels in type 1 diabetic patients is of great importance in the presence of possible faults caused by sensor measurements. Regulation of blood glucose levels using a type 3 fuzzy predictive controller in type 1 diabetic patients in the presence of sensor faults is considered. The proposed structure includes a main control structure and a virtual dynamic, in which the main structure includes a fuzzy identifier, predictive controller, and an adaptive compensator, and the virtual structure is used to identify the sensor faults. Glucose is unknown in the dynamics of type 1 diabetes and is estimated on-line using a type 3 fuzzy system. Also, Lyapunov stability analysis is used to design the adaptive compensator to ensure the stability of the closed-loop system. The proposed methodology is evaluated based on Bergman’s minimum model for different patients under various parametric uncertainties and disturbances.
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•An online identification method based on type 3 fuzzy system is presented.•Virtual dynamic is utilized for identifying and estimating the sensor fault values.•A predictive controller and an adaptive compensator are used in the structure.•Artificial intelligent pancreas for the insulin-glucose system in type 1 diabetes. |
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ISSN: | 0952-1976 |
DOI: | 10.1016/j.engappai.2024.109627 |