The path following control problem of the underactuated unmanned surface vessel (USV) is studied in this paper. An improved line-of-sight (LOS) guidance algorithm is proposed which can adjust adaptively based on the path following error. The global asymptotically stable path following controller is designed based on the nonlinear backstepping method and the Lyapunov stability theory. Firstly, the USV path following error model is established in the Serret-Frenet (SF) coordinate frame. The path following error in the inertial coordinate frame is transformed into the SF coordinate frame, which is used to define the path following control problem. Secondly, inspired by the traditional LOS guidance algorithm, the longitudinal path following error in the SF coordinate frame is introduced into the improved LOS guidance algorithm. This allows the algorithm to adjust adaptively to the desired path. Thirdly, in order to solve the underactuated problem of the USV path following control system, the tangential velocity of the desired path is designed as a virtual input. The underactuated problem is converted to a virtual fully actuated problem by designing the virtual control law for the tangential velocity. Finally, by combining backstepping design principles and the Lyapunov stability theory, the longitudinal thrust control law and the yaw torque control law are designed for the underactuated USV. Meanwhile, the global asymptotic stability of the path following error is proved. Simulation experiments demonstrate the effectiveness and reliability of the improved LOS guidance algorithm and the path following controller.


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    Title :

    Path Following Control of the Underactuated USV Based On the Improved Line-of-Sight Guidance Algorithm


    Contributors:
    Liu Tao (author) / Dong Zaopeng (author) / Du Hongwang (author) / Song Lifei (author) / Mao Yunsheng (author)


    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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