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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Stochastic Systems

    Background:

    • Stochastic nonlinear systems present challenges in control and state estimation.
    • Round-Robin (RR) scheduling protocols can induce periodic behavior in control systems.
    • Data congestion and energy saving are critical considerations in modern control applications.

    Purpose of the Study:

    • To develop a neural-network (NN)-based output-feedback control strategy for stochastic nonlinear systems operating under RR scheduling.
    • To design an NN-based observer for state reconstruction in protocol-induced periodic systems.
    • To ensure system stability and performance within finite and infinite horizons.

    Main Methods:

    • Implementation of RR scheduling protocols to create protocol-induced periodic systems.
    • Development of an NN-based observer with a novel adaptive tuning law for NN weights.
    • Utilizing matrix inequalities to determine observer gain based on system boundedness.
    • Employing an actor-critic NN scheme with a time-varying step length for finite-horizon control with terminal constraints.
    • Deriving sufficient conditions for the boundedness of NN weight estimation errors.

    Main Results:

    • An NN-based observer effectively reconstructs system states for periodic systems.
    • The observer gain is successfully obtained by solving matrix inequalities.
    • The actor-critic NN scheme addresses finite-horizon control with terminal constraints.
    • Sufficient conditions are established to guarantee the boundedness of estimation errors for NN weights.
    • Mean-square stability is demonstrated for both finite and infinite horizons.

    Conclusions:

    • The proposed NN-based output-feedback control scheme is effective for stochastic nonlinear systems under RR scheduling.
    • The method ensures stability and performance by addressing state estimation and control challenges in periodic systems.
    • The derived conditions simplify parameter determination and guarantee the boundedness of estimation errors.