Neural network prediction of the roll motion of a ship for intelligent course control

Abstract

For conventional ships, the mono-variable autopilot controls the heading of the ship in the presence of disturbances. During the heading control, there are many moments of time when the rudder command to control the yaw angle has a negative influence on roll oscillations. The prediction of the wave influence on the roll motion can be used to implement an intelligent heading control system, which is added to the mono-variable autopilot, generating only rudder commands with damping or non-increasing effects over roll movements. In this paper, aspects of roll angle and roll rate prediction using feed-forward neural networks are discussed. A neural network predictor of the roll rate, based on measured values of the roll angle, is proposed. The neural architecture is analyzed using different training data sets and noise conditions. The predictor has on-line adaptive characteristics and is working well even if both training and testing sets are affected by measurement noise. © Springer-Verlag Berlin Heidelberg 2007.