Feedforward Neural-Network Controller for Ship Steering

Abstract

A feedforward multilayer neural network is proposed to steer a ship to a given destination. For training and testing of the neural controller, the Nomoto's mathematical ship model, whose parameters change with the changing forward speed of the ship, is used. The neural controller is trained to steer the ship to a particular destination in a noise free environment. The trained controller is successful in controlling and guiding the ship to its destination in a noisy environment through number of gates. The performance of the neural network controller is tested for both severe process noise and measurement noise. The neural controller's ability to deal with noisy sea environments and inaccurate position measurements is very good.