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    This study introduces an adaptive dynamic programming (ADP) controller for nonlinear systems using event-sampled data. This approach optimizes control policies with reduced data transmission and computation.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Traditional optimal control requires full system state information and frequent updates.
    • Adaptive Dynamic Programming (ADP) offers a data-driven approach to approximate optimal control.
    • Event-based sampling reduces data load and computational burden in control systems.

    Purpose of the Study:

    • To develop an approximate optimal control strategy for nonlinear continuous-time systems using event-sampled data.
    • To relax the need for complete system dynamics knowledge through neural network identification.
    • To design an aperiodic update scheme for controller parameters.

    Main Methods:

    • Utilizing adaptive dynamic programming (ADP) with event-sampled state and input vectors.
    • Employing a neural network (NN) identifier for system dynamics with event-sampled inputs.
    • Approximating the Hamilton-Jacobi-Bellman equation solution using an event-sampled NN approximator.
    • Developing a novel adaptive event sampling condition to ensure stability and accuracy.

    Main Results:

    • An event-sampled ADP-based controller for nonlinear continuous-time systems was designed.
    • The controller utilizes NN identifier and value function approximator with aperiodic weight tuning.
    • A guaranteed positive lower bound on inter-sample time prevents accumulation points.
    • Local ultimate boundedness of the closed-loop system was demonstrated.

    Conclusions:

    • The proposed event-sampled ADP approach effectively achieves approximate optimal control for nonlinear systems.
    • The adaptive event sampling strategy maintains approximation accuracy and system stability.
    • This method offers a computationally efficient and robust control solution for systems with limited data availability.