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    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Autonomous underwater vehicles (AUVs) face challenges due to unknown nonlinear dynamics and uncertainties.
    • Adaptive control is crucial for maintaining stability and performance in uncertain AUV systems.

    Purpose of the Study:

    • To develop an adaptive learning control strategy for nonlinear AUVs with unknown uncertainties.
    • To ensure system stability and fast convergence of control parameters.

    Main Methods:

    • Approximation of unknown nonlinear functions using radial basis function neural networks (RBFNNs).
    • Design of weight updating laws via a gradient descent algorithm.
    • Incorporation of a command-filter-based technique to reduce computational load in backstepping control.

    Main Results:

    • The proposed control scheme guarantees semiglobal uniform ultimate boundedness (SUUB) of the AUV system.
    • Demonstrated fast convergence of RBFNN weight updating laws.
    • Simulation studies confirmed the effectiveness of the adaptive learning control strategy.

    Conclusions:

    • The developed adaptive learning control effectively addresses uncertainties in nonlinear AUVs.
    • The integration of RBFNNs and command filters provides a robust and computationally efficient solution.
    • The method offers a promising approach for advanced AUV control applications.