Aiming at the control problem of nonlinear uncertain systems with asymmetric saturated actuators and u nknown external disturbances, a composite control method integrating dynamic surface control (DSC), ad aptive neural network estimation, and a nonlinear saturation compensation mechanism is proposed. In the scenarios of ship course and trajectory tracking, the system faces multiple challenges such as symmetric and asymmetric actuator saturation, as well as unknown external disturbances. Radial basis function (R BF) neural networks are utilized for online approximation of unknown nonlinear functions and external d isturbances. Combined with dynamic surface technology, the problem of "explosion of complexity" in tra ditional backstepping control is eliminated. A nonlinear function with inverse correlation to error gain is designed to dynamically adjust the control gain, balancing the requirements of tracking accuracy and sat uration suppression. Furthermore, a Gaussian error function is introduced to construct a continuously diff erentiable asymmetric saturation model. An auxiliary dynamic system is integrated to compensate for the saturation nonlinear effect, achieving smooth amplitude limitation of rudder angle commands. Comparati ve MATLAB simulation results demonstrate that the course tracking error is reduced by 1°, the fluctuati on amplitude of the rudder angle is decreased by approximately 50%, the number of rudder angle satura tion events is reduced by about 60%, and the error convergence time is shortened by roughly 30%. The proposed composite control method effectively addresses the issues of asymmetric saturation and externa l disturbances, significantly enhancing the accuracy and robustness of the ship course control system.

A robust adaptive control approach is proposed for underactuated surface ship linear path-tracking control system based on the backstepping control method and Lyapunov stability theory. By employing T-S fuzzy system to approximate nonlinear uncertainties of the control system, the proposed scheme is developed by combining “dynamic surface control” (DSC) and “minimal learning parameter” (MLP) techniques. The substantial problems of “explosion of complexity” and “dimension curse” existed in the traditional backstepping technique are circumvented, and it is convenient to implement in applications. In addition, an auxiliary system is developed to deal with the effect of input saturation constraints. The control algorithm avoids the singularity problem of controller and guarantees the stability of the closed-loop system. The tracking error converges to an arbitrarily small neighborhood. Finally, MATLAB simulation results are given from an application case of Dalian Maritime University training ship to demonstrate the effectiveness of the proposed scheme.