Aiming at the trajectory tracking control problem of underactuated autonomous underwater vehicles (AUVs) with unknown dynamic model parameters and time-varying ocean disturbances, a dynamic surface adaptive control strategy based on radial basis function neural network is proposed to complete the desired three-dimensional trajectory tracking. Firstly, an output redefinition method is used to solve the underactuated problem. Secondly, a dynamic surface control method is used to overcome the “explosion of complexity” problem in traditional backstepping control, and complexity of the control law design is simplified. At the same time, a nonlinear damping term is added in control law to overcome unknown external disturbances. Finally, unknown dynamic model parameters are approximated by a neural network, and the neural network weights are estimated online by an adaptive law. The Lyapunov theory is used to prove that all signals of closed-loop system are semi-globally uniformly bounded, and effectiveness of the control law is verified by comparative simulations.


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    Title :

    Adaptive Neural Network Dynamic Surface Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles


    Contributors:
    Jiang, Guohong (author) / Joo Er, Meng (author) / Gong, Huibin (author) / Wang, Siqi (author)


    Publication date :

    2024-09-20


    Size :

    3326603 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English