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    Area of Science:

    • Control Theory
    • Machine Learning
    • Nonlinear Systems

    Background:

    • Input saturation poses challenges in controlling nonlinear systems.
    • Existing adaptive dynamic programming (ADP) methods may have limitations in convergence speed and triggering efficiency.
    • Data-driven approaches offer potential for robust control strategies.

    Purpose of the Study:

    • To develop a data-driven event-triggered adaptive dynamic programming (ADP) control strategy for nonlinear systems experiencing input saturation.
    • To improve the convergence rate of the error system using a dynamic penalty factor.
    • To design a novel triggering mechanism that minimizes redundant events.

    Main Methods:

    • Utilizing adaptive dynamic programming (ADP) with a modified index to establish a global optimal data-driven control law.
    • Implementing a dynamic penalty factor to accelerate error convergence.
    • Developing a new event-triggering mechanism to reduce unnecessary triggering.
    • Applying Lyapunov stability analysis to ensure uniform ultimate boundedness of the error system.

    Main Results:

    • A data-driven optimal control law was successfully established for nonlinear systems with input saturation.
    • The dynamic penalty factor demonstrated accelerated error convergence compared to constant factors.
    • The proposed triggering mechanism effectively reduced redundant triggering events.
    • Uniform ultimate boundedness of the error system was theoretically proven.

    Conclusions:

    • The presented data-driven event-triggered ADP control scheme effectively addresses input saturation in nonlinear systems.
    • The enhanced convergence and reduced triggering contribute to more efficient and stable control.
    • The method's validity is confirmed through simulation examples.