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    Area of Science:

    • Robotics and Control Systems
    • Adaptive Control Theory
    • Nonlinear System Dynamics

    Background:

    • Tracking control for Euler-Lagrange systems is challenging due to unknown dynamics and target trajectories.
    • Existing methods often require detailed system information or lack guaranteed constraint satisfaction.

    Purpose of the Study:

    • To develop a neuroadaptive tracking control scheme for Euler-Lagrange systems capable of handling unknown targets.
    • To ensure prescribed tracking performance with finite-time convergence and constraint satisfaction.

    Main Methods:

    • A memory-based trajectory predictor reconstructs unknown target behavior.
    • Integration of speed transformation and barrier Lyapunov functions (BLF) for stability analysis.
    • Introduction of a virtual parameter to simplify online parameter updates.

    Main Results:

    • Achieved close target tracking without prior knowledge of system dynamics or target trajectory.
    • Tracking error converges to a prescribed precision within finite time at an assignable rate.
    • Full-state constraints are guaranteed to be never violated; all signals remain bounded.

    Conclusions:

    • The proposed neuroadaptive control scheme offers a robust, computationally efficient, and structurally simple solution for tracking control.
    • Demonstrated feasibility and effectiveness through simulations, confirming prescribed performance and constraint adherence.