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    Area of Science:

    • Control Systems Engineering
    • Networked Systems
    • Estimation Theory

    Background:

    • Complex networks (CNs) face challenges in transmitting measurements over shared networks due to bandwidth limitations and intermittent data.
    • Existing methods struggle with data loss and variable transmission intervals, impacting estimation accuracy.

    Purpose of the Study:

    • To design zonotopic set-membership fusion estimation (SMFE) algorithms for complex networks (CNs) with intermittent data transmissions.
    • To enhance measurement utilization using buffered data and address transmission intervals exceeding sampling periods.
    • To ensure the boundedness of estimation uncertainty in networked systems.

    Main Methods:

    • A batch processing method is proposed to concurrently process input data by iterating the state equation of the CN.
    • Zonotopic set-membership estimation (SME) techniques are employed to design the SMFE algorithms.
    • Sufficient criteria are established to guarantee uniform boundedness of output zonotope sizes.

    Main Results:

    • Novel SMFE algorithms are developed for CNs utilizing buffered, intermittently transmitted data.
    • The proposed algorithms effectively handle transmission intervals larger than sampling periods.
    • Guarantees for uniformly bounded zonotope sizes are established, ensuring estimation stability.

    Conclusions:

    • The developed SMFE algorithms enhance data utilization and estimation performance in complex networks with communication constraints.
    • The findings provide a robust framework for state estimation in intermittently connected systems.
    • Numerical examples validate the effectiveness and practical applicability of the proposed algorithms.