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    This study introduces a novel neural network (NN) tracking control for uncertain nonlinear systems. The new method ensures stability and minimizes output errors for systems with unknown disturbances.

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    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Artificial Intelligence

    Background:

    • Global tracking control is crucial for uncertain nonlinear systems.
    • Existing neural network (NN) control methods face challenges with unknown disturbance bounds and unmeasured states.
    • Output feedback control is essential for practical applications where all states are not measurable.

    Purpose of the Study:

    • To develop a robust global neural network (NN) tracking control strategy for uncertain nonlinear systems with unknown disturbance bounds.
    • To address challenges posed by unmeasured states and system uncertainties in output feedback control.
    • To ensure the globally uniformly ultimately bounded stability of all closed-loop signals.

    Main Methods:

    • A novel controller combining a neural network (NN) approximator and a robust component.
    • A smooth switching function to transition between the NN and robust controllers.
    • Lyapunov stability analysis to rigorously prove system stability and error convergence.

    Main Results:

    • The proposed control scheme guarantees global uniform ultimate boundedness for all closed-loop signals.
    • Output tracking errors are shown to converge to an arbitrarily small neighborhood.
    • The effectiveness of the control strategy is validated through numerical and practical examples.

    Conclusions:

    • The developed NN tracking control method offers a robust solution for uncertain nonlinear systems under disturbances.
    • The smooth switching mechanism enhances stability and performance.
    • The approach provides a reliable framework for achieving precise tracking control in complex systems.