Generalized Type-2 Fuzzy Control for Type-I Diabetes: Analytical Robust System

Shu-Rong Yan, Khalid A. Alattas, Mohsen Bakouri, Abdullah K. Alanazi, Ardashir Mohammadzadeh, Saleh Mobayen, Anton Zhilenkov and Wei Guo
Additional contact information
Shu-Rong Yan: National Key Project Laboratory, Jiangxi University of Engineering, Xinyu 338000, China
Khalid A. Alattas: Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
Mohsen Bakouri: Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Majmaah 11952, Saudi Arabia
Abdullah K. Alanazi: Department of Chemistry, Faculty of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Ardashir Mohammadzadeh: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Saleh Mobayen: Future Technology Research Center, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Anton Zhilenkov: Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 Saint-Petersburg, Russia
Wei Guo: School of Credit Management, Guangdong University of Finance, Guangzhou 510521, China

Mathematics, 2022, vol. 10, issue 5, 1-20

Abstract: The insulin injection rate in type-I diabetic patients is a complex control problem. The mathematical dynamics for the insulin/glucose metabolism can be different for various patients who undertake different activities, have different lifestyles, and have other illnesses. In this study, a robust regulation system on the basis of generalized type-2 (GT2) fuzzy-logic systems (FLSs) is designed for the regulation of the blood glucose level. Unlike previous studies, the dynamics of glucose–insulin are unknown under high levels of uncertainty. The insulin-glucose metabolism has been identified online by GT2-FLSs, considering the stability criteria. The learning scheme was designed based on the Lyapunov approach. In other words, the GT2-FLSs are learned using adaptation rules that are concluded from the stability theorem. The effect of the dynamic estimation error and other perturbations, such as patient activeness, were eliminated through the designed adaptive fuzzy compensator. The adaptation laws for control parameters, GT2-FLS rule parameters, and the designed compensator were obtained by using the Lyapunov stability theorem. The feasibility and accuracy of the designed control scheme was examined on a modified Bergman model of some patients under different conditions. The simulation results confirm that the suggested controller has excellent performance under various conditions.

Keywords: fuzzy logic systems; generalized type-2 fuzzy sets; adaptive rules; machine learning; glucose–insulin; stability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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