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A Scalable Algorithm for Event-Triggered State Estimation With Unknown Parameters and Switching Topologies Over

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    This study introduces an event-triggered distributed state estimation method for nonlinear stochastic systems with unknown parameters in sensor networks. The approach reduces resource use while ensuring estimation accuracy and scalability.

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    Area of Science:

    • Control Systems Engineering
    • Networked Systems
    • Stochastic Systems

    Background:

    • Distributed state estimation is crucial for networked systems.
    • Unknown parameters and switched topologies complicate estimation.
    • Event-triggered communication conserves network resources.

    Purpose of the Study:

    • To develop an event-triggered distributed state estimator for discrete-time nonlinear stochastic systems.
    • To simultaneously estimate system states and identify unknown parameters.
    • To ensure stability and reduce communication load in sensor networks.

    Main Methods:

    • Design of an event-triggered communication strategy.
    • Development of a distributed state estimator based on input-to-state stability.
    • Establishment of sufficient conditions using average dwell time.
    • Formulation of estimator gains via matrix inequalities independent of network size.

    Main Results:

    • Guaranteed boundedness of estimation errors in the mean-square sense.
    • Effective identification of unknown system parameters.
    • Demonstrated scalability of the proposed estimator design.
    • Validation of the scheme through simulations.

    Conclusions:

    • The proposed event-triggered distributed state estimation scheme is effective for nonlinear stochastic systems.
    • The method enhances resource efficiency and ensures estimation accuracy.
    • The design is scalable and robust to switched topologies.