This paper presents a weight optimization method for a nonlinear model predictive controller (NMPC) based on the genetic algorithm (GA) for ship trajectory tracking. The weight coefficients Q and R of the objective function in NMPC are obtained via the real-time optimization of the genetic algorithm instead of the trial and error method, which improves the efficiency and accuracy of the controller. In addition, targeted improvements are made to the internal crossover operator, mutation operator, crossover rate, and mutation rate of the genetic algorithm. The simulation comparison of trajectory tracking between NMPC with real-time-optimized weight coefficients and the one with constant coefficients is performed. Finally, the simulation result shows that the controller with real-time-optimized weight coefficients has a better trajectory tracking effect than that with constant weight coefficients.

This paper presents a novel adaptive Line-Of-Sight (ALOS) path following algorithm for underactuated unmanned surface vehicle (USV) in the presence of external random disturbance. Considering the changing of tracking errors, the ALOS controller is designed with an adaptively adjusting forward-looking distance to output the desired heading of the USV to rapidly approach the path to follow. To eliminate the influence of external disturbances, the nonlinear disturbance observer (NDO) is designed to obtain the disturbance estimate of yaw moment. Furthermore, based on the desired heading obtained by ALOS, the disturbance estimate and the desired surge speed, the heading and surge backstepping controllers are designed, respectively. The stability of the control system is proved by Lyapunov direct method. Simulation results verify the effectiveness and robustness of the control law.