The article presents various configurations of nonlinear ship course controllers. The controllers were designed based on backstepping method, the PD and sliding mode control. The controller versions at the design stage take into account the dynamic properties of the steering gear. A simplified simulation model of the B-481 type vessel, including wave and wind effects, was applied to simulate the control algorithm by using time domain analysis. The results of computer simulations have shown the advantages of control algorithms taking into account the dynamics of steering gear such as the increasing the economic efficiency of system operation, the reducing rudder activity and increasing the average speed of the vessel during the voyage

The goal of research presented in this article is to check if a neuroevolutionary method with direct encoding is able to be a part of autopilot of the vessel. One of the important tasks of vessel autopilots is to keep a course as straight as possible or to bring the ship back on the route as efficiently as possible. In this paper, the adaptive neuroevolutionary autopilot is described and tested on a simulation model of a ferry. Neuroevolution is a combination of two different but related fields of artificial machine learning: evolution and neural networks. The combined method is very flexible and can be applied to other ship control tasks. The results of computer simulation of the neuroevolutionary course-keeping system have been included.