In this paper, an Active Disturbance Rejection Control (ADRC) parameter tuning method optimized by Grey Wolf Optimization (GWO) is proposed for the course control and path following of the ship. Firstly, a ship course controller based on ADRC and the first-order Nomoto model is established. Secondly, GWO is used to optimize the parameters of ADRC. Finally, the Integral Line-Of-Sight (ILOS) guidance law is used to navigate the ship to accomplish the purpose of path following. The simulation results indicate that the proposed controller has good characteristics with small overshoot angle, high precision, fast response and smooth rudder angle input.
Motion control is an important issue for the surface ships. It includes course control, path following, dynamic positioning, trajectory tracking, etc. (Liu et al., 2021). The motion control of an underactuated ship is normally achieved by propeller(s) and rudder(s). The model describing ship motions may suffer from parameter uncertainties, nonlinear characteristics, and even unmodelled dynamics under time-varying and rough external disturbances (Liu et al., 2020). Therefore, designing a high-performance ship course controller is a challenging task, and several methods have been proposed to solve these problems. For instance, a disturbance rejection course controller was introduced to address unknown disturbances (Lei et al, 2015). In addition, robust integral backstepping, synergetic, and terminal synergetic controllers were proposed for obtaining good course control performances and for reducing the energy consumption of ships in course control (Islam et al., 2021). The active disturbance rejection control (ADRC) is proposed by Han (2009). The advantage of ADRC is that it does not need to know precisely the dynamic characteristics of the controlled object and the effect of external disturbances, so it can suppress all disturbances at the same time. The ADRC is improved from Proportion-Integration-Differentiation (PID) and the core idea of PID error feedback control. Li et al. (2013) proposed a composite control method of ADRC with sliding mode in order to improve the performance of the closed-loop system and to solve the constraint condition problem of surface ships. Li et al. (2018) developed a course controller based on ADRC to consider the uncertain parameters and wave disturbances. Zhang et al. (2021) proposed an ADRC algorithm based on nonlinear feedback, aiming at problems considering such as external disturbance, internal model uncertainty in the course control process of ships, and introduced a zero-order hold to reduce steering frequency and rudder wear. In the process of designing ADRC controller, many parameters need to be tuned, so many scholars used optimization algorithms to adjust the parameters of ADRC. For instance, Yang et al. (2022) proposed an improved ADRC based on particle swarm optimization.
