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Neural-Network-Based Set-Membership Fault Estimation for 2-D Systems Under Encoding-Decoding Mechanism.

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    This study introduces a novel neural-network-based estimator for nonlinear 2-D systems, ensuring accurate state and fault estimation despite limited communication bandwidth. The method optimizes data transmission and guarantees estimation errors within a defined ellipsoidal set.

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    Area of Science:

    • Control Systems Engineering
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Nonlinear 2-D shift-varying systems present challenges for state and fault estimation.
    • Limited bandwidth communication networks complicate data transmission for estimators.
    • Ensuring estimation error bounds is critical for system reliability.

    Purpose of the Study:

    • To develop a neural-network (NN)-based set-membership estimator for simultaneous state and fault estimation.
    • To address communication constraints by proposing a novel encoding-decoding mechanism.
    • To guarantee that estimation errors remain within an optimized ellipsoidal set.

    Main Methods:

    • Utilizing a neural-network (NN) approach for set-membership estimation.
    • Implementing a new encoding-decoding mechanism to manage limited bandwidth communication.
    • Employing mathematical induction and convex optimization techniques.

    Main Results:

    • Sufficient conditions for the existence of the set-membership estimator were derived.
    • Estimator gains and NN tuning scalars were determined via optimization problems.
    • The proposed method effectively estimates system states and faults within ellipsoidal bounds.

    Conclusions:

    • The developed NN-based set-membership estimator effectively handles state and fault estimation for nonlinear 2-D systems under bandwidth constraints.
    • The novel encoding-decoding mechanism reduces communication load and enhances security.
    • The method provides guaranteed bounds on estimation errors, crucial for robust control applications.