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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Token-Bucket-Protocol-Based Recursive Remote State Estimation for Complex Networks Under Amplify-and-Forward Relays.

Tong-Jian Liu, Zidong Wang, Yang Liu

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    Area of Science:

    • Control Systems Engineering
    • Networked Systems
    • Signal Processing

    Background:

    • Networked state estimation is crucial for complex systems.
    • Existing methods often overlook data transmission constraints.
    • The token bucket protocol (TBP) introduces stochasticity in signal transmission.

    Purpose of the Study:

    • To develop a recursive remote estimation algorithm for nonlinear complex networks under TBP and AF relays.
    • To analyze the impact of TBP on networked state estimation performance.
    • To minimize estimation error covariance.

    Main Methods:

    • An extended Kalman filter-based recursive estimator was designed.
    • Riccati-like difference equations were solved to bound error covariance.
    • Estimator gain was optimized to minimize the error bound.
    • Stochastic modeling of token consumption and channel coefficients was employed.

    Main Results:

    • A novel recursive estimator was proposed for TBP-constrained systems.
    • An upper bound for prediction/estimation error covariance was derived.
    • The influence of TBP on estimation performance was quantified.
    • Numerical simulations validated the estimator's effectiveness.

    Conclusions:

    • The proposed estimator effectively handles nonlinear complex networks with TBP and AF relays.
    • The TBP significantly impacts networked state estimation performance.
    • The developed method provides a robust approach to remote estimation under communication constraints.