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

Author

Listed:
  • 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
    Department of Physics, College of Arts, Fezzan University, Traghen 71340, Libya)

  • 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
    School of Engineering and Technology, 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)

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.

Suggested Citation

  • Shu-Rong Yan & Khalid A. Alattas & Mohsen Bakouri & Abdullah K. Alanazi & Ardashir Mohammadzadeh & Saleh Mobayen & Anton Zhilenkov & Wei Guo, 2022. "Generalized Type-2 Fuzzy Control for Type-I Diabetes: Analytical Robust System," Mathematics, MDPI, vol. 10(5), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:690-:d:756433
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    References listed on IDEAS

    as
    1. Pei Liang & Junhua Hu & Bo Li & Yongmei Liu & Xiaohong Chen, 2020. "A group decision making with probability linguistic preference relations based on nonlinear optimization model and fuzzy cooperative games," Fuzzy Optimization and Decision Making, Springer, vol. 19(4), pages 499-528, December.
    2. Junhua Hu & Panpan Chen & Yan Yang, 2019. "An Interval Type-2 Fuzzy Similarity-Based MABAC Approach for Patient-Centered Care," Mathematics, MDPI, vol. 7(2), pages 1-25, February.
    3. Sayyar Ahmad & Charrise M. Ramkissoon & Aleix Beneyto & Ignacio Conget & Marga Giménez & Josep Vehi, 2021. "Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts," Mathematics, MDPI, vol. 9(11), pages 1-15, May.
    Full references (including those not matched with items on IDEAS)

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