Ship Heading Controller Design Based on Neural Networks

The ship steering control to yaw motions of the marine vessels is a very frustrating assignment. Thus, this article presents the design of ship heading control system based on radial basis function neural (RBFNN) networks. The controller employs error signal and respective derivative as inputs to the RBFNN. Backward difference approximation techniques are employed to compute the error signal. Simulation results demonstrate how the control system regulates the ship heading and shows the response characteristics of the controller. The results obtained prove that the controller can perform well compared to other methods.