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Adaptive Event-Triggered Transmission Scheme and H

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    This study introduces adaptive event-triggered mechanisms (AETMs) for distributed H∞ filtering in nonlinear systems with switching network topologies. The approach optimizes data transmission for improved filtering performance.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Systems Analysis
    • Networked Systems

    Background:

    • Distributed filtering is crucial for networked systems.
    • Handling time-varying and switching network topologies presents challenges.
    • Limited bandwidth necessitates efficient data transmission strategies.

    Purpose of the Study:

    • To develop a distributed adaptive event-triggered H∞ filtering method.
    • To address systems with sector-bounded nonlinearities and Markovian switching topologies.
    • To design filters and event parameters simultaneously for improved performance.

    Main Methods:

    • Utilizing adaptive event-triggered mechanisms (AETMs) with dynamic thresholds.
    • Modeling the filtering network as a Markov jump nonlinear system.
    • Applying stochastic Markov stability theory and linear matrix inequality (LMI) techniques.

    Main Results:

    • A co-design algorithm for H∞ filters and event parameters was developed.
    • The proposed AETM enhances data scheduling and transmission efficiency.
    • Guaranteed H∞ filtering performance under dynamic network conditions was demonstrated.

    Conclusions:

    • The developed method is effective for distributed adaptive event-triggered H∞ filtering.
    • The approach is applicable to nonlinear systems with switching topologies.
    • Simulation results validate the practical applicability of the filtering network.