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    This study introduces novel stochastic event-triggered schemes for remote state estimation in nonlinear systems, optimizing data transmission in wireless sensor networks. These methods balance communication efficiency with accurate state estimation quality.

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    Area of Science:

    • Control Systems Engineering
    • Wireless Sensor Networks
    • Nonlinear System Analysis

    Background:

    • Remote state estimation for nonlinear systems is challenging due to non-Gaussian properties and communication constraints.
    • Existing deterministic event-triggered schemes may not optimally handle the non-Gaussian nature of measurements.

    Purpose of the Study:

    • To develop novel stochastic event-triggered scheduling schemes for efficient remote state estimation in nonlinear systems.
    • To design and analyze nonlinear state estimators that maintain data integrity while minimizing transmission.

    Main Methods:

    • Utilizing unscented transformation to approximate non-Gaussian probability distributions.
    • Developing uncontrollable and controllable stochastic event-triggered scheduling schemes.
    • Designing nonlinear state estimators based on the unscented Kalman filter.
    • Analyzing stability and convergence using estimation error and covariance.

    Main Results:

    • Proposed stochastic event-triggered schemes effectively reduce data transmission in communication-limited wireless sensor networks.
    • The new schemes demonstrate superiority in maintaining the Gaussian property of the innovation process compared to deterministic methods.
    • Stability and convergence of the designed nonlinear state estimators are rigorously established.
    • A tunable parameter allows for balancing communication rate and estimation quality.

    Conclusions:

    • The developed stochastic event-triggered schemes offer an effective solution for remote state estimation in nonlinear systems.
    • These methods provide a practical approach to manage data transmission in wireless sensor networks without significant loss of estimation accuracy.
    • The findings enable a flexible trade-off between communication overhead and the quality of state estimation.