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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 a novel predictor-based neural dynamic surface control (PNDSC) method for uncertain nonlinear systems. The PNDSC method uses prediction errors for neural network updates, avoiding high-frequency oscillations common in other approaches.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Traditional neural dynamic surface control (NDSC) methods often struggle with high-frequency oscillations.
    • Updating neural network weights based on tracking errors can lead to performance limitations in uncertain systems.

    Purpose of the Study:

    • To develop a novel predictor-based neural dynamic surface control (PNDSC) design for uncertain nonlinear systems.
    • To improve the identification of system dynamics and reduce oscillations compared to existing NDSC methods.

    Main Methods:

    • A predictor is introduced for each subsystem to generate prediction errors.
    • Prediction errors are utilized to update neural network adaptation laws, differing from traditional tracking error-based methods.
    • The control scheme is extended to include observer feedback and analyze robustness against measurement noise.

    Main Results:

    • The proposed PNDSC method achieves smooth and fast identification of system dynamics.
    • High-frequency oscillations, a common issue in classical NDSC, are effectively mitigated.
    • The extended PNDSC with observer feedback demonstrates robustness against measurement noise.

    Conclusions:

    • The predictor-based neural dynamic surface control (PNDSC) offers a superior approach for controlling uncertain nonlinear systems.
    • The method provides enhanced performance by avoiding oscillations and maintaining robustness.
    • Numerical and experimental validation confirms the efficacy of the PNDSC architecture.