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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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    This study introduces an event-triggered adaptive neural network control for nonlinear large-scale systems with constraints and hysteresis. The method reduces communication load while ensuring system stability and preventing constraint violation.

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    Area of Science:

    • Control Engineering
    • Artificial Intelligence
    • Nonlinear Systems

    Background:

    • Nonlinear large-scale systems (LSSs) present significant control challenges.
    • Full-state constraints and unknown hysteresis complicate control design.
    • Existing methods may incur high communication burdens.

    Purpose of the Study:

    • To develop an event-triggered adaptive neural network control strategy for nonlinear LSSs.
    • To address challenges posed by full-state constraints and unknown hysteresis.
    • To minimize communication load and signal transmission frequency.

    Main Methods:

    • Utilizing radial basis function neural networks (NNs) for state observation.
    • Implementing an event-triggered mechanism with a one-bit encoding-decoding strategy.
    • Employing backstepping control and barrier Lyapunov functions.
    • Compensating for unknown hysteresis by estimating its parameters.

    Main Results:

    • Successfully addressed the algebraic loop problem.
    • Reduced communication burden through event-triggered and one-bit transmission.
    • Prevented violation of full-state constraints.
    • Ensured semiglobal ultimate uniform boundedness of all signals.

    Conclusions:

    • The proposed event-triggered adaptive NN control is effective for nonlinear LSSs.
    • The strategy efficiently handles constraints and unknown hysteresis.
    • Demonstrated effectiveness through simulation examples.