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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Adjustable-Error-Based Adaptive Neural Network Tracking Control for Uncertain Nonlinear Systems.

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    This study introduces an adjustable-error neural network (NN) for adaptive neural control. This innovation enhances approximation accuracy and tracking error convergence in uncertain nonlinear systems.

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    Area of Science:

    • Control Engineering
    • Artificial Intelligence
    • Nonlinear System Analysis

    Background:

    • Traditional neural network (NN) control struggles with adjustable approximation errors.
    • This limitation impacts precision and tracking accuracy in uncertain nonlinear systems.
    • Existing methods lack flexibility in managing the error between NN approximators and unknown functions.

    Purpose of the Study:

    • To develop an adjustable-error neural network (NN) approximator.
    • To integrate this approximator into an adaptive neural tracking controller for uncertain nonlinear systems.
    • To enhance approximation accuracy and tracking error convergence.

    Main Methods:

    • Designed an adjustable-error NN approximator with adjustable parameters.
    • Incorporated the approximator into an adaptive neural tracking controller.
    • Utilized Lyapunov stability theory for system analysis and tracking error convergence.

    Main Results:

    • Achieved higher tracking error accuracy compared to traditional methods.
    • Demonstrated improved approximation accuracy for unknown nonlinear functions.
    • Verified the closed-loop system stability and tracking error convergence.

    Conclusions:

    • The proposed adjustable-error NN approximator enhances adaptive neural control.
    • The novel controller design offers superior performance in uncertain nonlinear systems.
    • Simulation and experimental results validate the effectiveness of the proposed scheme.