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    This study introduces a new adaptive control strategy for uncertain nonlinear systems. The controller ensures system states remain within user-defined bounds while achieving asymptotic tracking performance.

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    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Adaptive Control

    Background:

    • Controlling multi-input-multi-output uncertain nonlinear systems with disturbances is challenging.
    • Existing methods often focus on stabilization, not constraint satisfaction.

    Purpose of the Study:

    • To develop a novel adaptive control strategy for tracking control.
    • To ensure system states remain within user-defined time-varying constraints.
    • To achieve asymptotic tracking performance for uncertain nonlinear systems.

    Main Methods:

    • An error transformation technique to create an equivalent nonlinear system.
    • Online neural network (NN) approximation to handle system uncertainties.
    • Robust integral of the sign of the error signal for disturbance compensation.
    • Lyapunov method for theoretical guarantees.

    Main Results:

    • System states converge asymptotically and stay within user-defined time-varying bounds.
    • Asymptotic tracking performance is theoretically guaranteed.
    • All closed-loop signals are bounded.
    • Prior knowledge of bounds for uncertain terms is not required.

    Conclusions:

    • The proposed adaptive control strategy effectively manages uncertain nonlinear systems.
    • It successfully enforces user-defined state constraints and achieves asymptotic tracking.
    • Simulation results validate the controller's performance.