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Asynchronous Sampled-Data Filtering Design for Fuzzy-Affine-Model-Based Stochastic Nonlinear Systems.

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    This study addresses asynchronous sampled-data filtering for Itô stochastic nonlinear systems using Takagi-Sugeno fuzzy models. Novel methods ensure effective filtering despite data delays, validated by simulations.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear System Analysis
    • Stochastic Processes

    Background:

    • Asynchronous sampled-data systems present challenges in filtering due to data acquisition delays.
    • Itô stochastic nonlinear systems require specialized filtering techniques to handle randomness and nonlinearity.
    • Takagi-Sugeno fuzzy-affine models offer a framework for approximating complex nonlinear dynamics.

    Purpose of the Study:

    • To design an asynchronous sampled-data filter for Itô stochastic nonlinear systems.
    • To address the impact of sample-and-hold behavior on measurement output.
    • To develop novel filtering design methods using advanced mathematical tools.

    Main Methods:

    • Utilizing Takagi-Sugeno fuzzy-affine models to represent system dynamics.
    • Modeling asynchronous sampled-data behavior using an input delay approach.
    • Developing a piecewise quadratic Lyapunov-Krasovskii functional.
    • Employing linearization and convexification techniques for filter design.

    Main Results:

    • New results for asynchronous sampled-data filtering design are proposed.
    • The proposed method effectively handles input delays caused by sample-and-hold behavior.
    • Simulation studies demonstrate the effectiveness of the developed filtering approach.

    Conclusions:

    • The proposed asynchronous sampled-data filtering design is effective for Itô stochastic nonlinear systems.
    • The novel Lyapunov-Krasovskii functional and convexification techniques contribute to improved filtering performance.
    • The method provides a robust solution for systems with asynchronous data acquisition.