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    Area of Science:

    • Control Systems Engineering
    • Nonlinear System Analysis
    • Fuzzy Logic Systems

    Background:

    • Large-scale nonlinear systems pose significant control challenges.
    • Polynomial Takagi-Sugeno (T-S) fuzzy systems offer improved modeling accuracy.
    • Controller synthesis for uncertain polynomial T-S systems is an underexplored area.

    Purpose of the Study:

    • To propose a novel controller synthesis method for stabilizing uncertain nonlinear large-scale polynomial T-S fuzzy systems.
    • To address the challenge of uncertainties in controller design for these systems.
    • To reduce modeling errors and the number of fuzzy rules compared to conventional methods.

    Main Methods:

    • Utilizing Lyapunov theory for stability analysis.
    • Employing sum-of-square (SOS) techniques for controller design.
    • Applying the S-procedure to handle system uncertainties.

    Main Results:

    • Development of a controller synthesis framework for uncertain polynomial T-S fuzzy systems.
    • Derivation of conditions for controller synthesis through main theorems.
    • Demonstration of controller effectiveness via two illustrative examples.

    Conclusions:

    • The proposed method provides an effective approach for stabilizing uncertain nonlinear large-scale polynomial T-S fuzzy systems.
    • The technique successfully mitigates the impact of uncertainties.
    • The study highlights the advantages of polynomial T-S fuzzy modeling for complex systems.