In order to reduce the difficulty of control design, a low-pass filter is usually used to replace the derivation of the virtual variables. Dynamic surface control (DSC) uses a low-pass filter to avoid the differential explosion phenomenon in the design process. The dynamic surface control method was applied in [24] to solve the control problem of a class of nonlinear systems. In [25], two cascaded subsystems were formed by coordinate conversion of the system error model, and the DSC method was introduced into the trajectory tracking control of the USV. In [26], an adaptive dynamic sliding mode control based on the idea of the backstepping method was proposed to solve the trajectory tracking control problem for an unmanned underwater vessel (UUV) for modeling parameter uncertainty and environmental perturbation. Due to the inevitable calculation of multiple design virtual control variables in the design process, the calculation complexity can be reduced using the differential substitution characteristics of the model based on a biologically inspired model. In [27], an adaptive backstep sliding mode controller was designed based on a biologically inspired model to realize the trajectory tracking of a USV. The underactuated USV has no lateral thrusters to control the lateral displacement. The objective of control of underactuated vessels is to solve the problem that occurs when a fully actuated USV breaks down, or in the absence of bow thrusters, where only the propellers and the yaw moment are utilized to achieve the purpose of trajectory tracking control. An underactuated USV with a forward speed is inevitably affected by the wind, waves, currents, and other unknown disturbances. If these unknown disturbances cannot be handled properly, the functionality of the underactuated USVs will be much reduced. Measuring the unknown disturbance is the key to underactuated USV trajectory tracking control [28]. In addition, in sliding mode control, the upper and lower bounds of the disturbance are usually estimated, and a larger gain parameter is designed to solve the disturbance problem. By adopting a disturbance observer, the value of the disturbance can be estimated, to better solve the disturbance problem. In [29], aiming at the formation control problem of an underactuated USV, an adaptive finite-time disturbance observer was introduced to observe external environmental disturbances. In [30], based on fuzzy theory, a fuzzy unknown observer was proposed to estimate the compound disturbance of the system, to solve the underactuated USV path tracking control problem. By treating external disturbances as periodic disturbances, the Fourier series expansion of periodic disturbances and Lyapunov theory were used to obtain the optimal estimation of environmental disturbances [31]. In [32], model predictive control was used to devise trajectory tracking control, and nonlinear disturbance observers were introduced to compensate for unknown disturbances, to achieve optimal trajectory control. In [33], due to the difficulty of obtaining information about the underactuated state, a sliding mode observer was designed to estimate the linear velocity and angular velocity, combined with a backstepping sliding mode control principle to realize the underactuated UUV trajectory tracking control. Since an RBF network is able to approximate arbitrary functions, an adaptive neural network was designed [34] to approximate unknown external environmental disturbances, in combination with a high-gain observer to achieve fast feedback, to ensure the transient performance of underactuated USVs. In approximating unknown disturbances with neural networks [35; 36], the choice of the weighting matrix and the number of nodes in the implicit layer is a difficult issue. The nonlinear disturbance observer design is transformed into a linear matrix inequality, making the selection of parameters easier, while the speed of convergence is related to the selection parameters of the Lyapunov function coefficients.