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

Authors:
Viorel Nicolau;Vasile Palade;Dorel Aiordachioaie;Constantin Miholca
Affiliations:
"Dunarea de Jos" University of Galati, Department of Electronic and Telecommunications, Galati, Romania;Oxford University, Computing Laboratory, Oxford, United Kingdom;"Dunarea de Jos" University of Galati, Department of Electronic and Telecommunications, Galati, Romania;"Dunarea de Jos" University of Galati, Department of Electronic and Telecommunications, Galati, Romania
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

Citing 8
Cited 0

Rudder roll stabilization for ships

Automatica (Journal of IFAC)
Approximation capabilities of multilayer feedforward networks

Neural Networks
Original Contribution: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

Neural Networks
Rational function neural network

Neural Computation
An orthogonal neural network for function approximation

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural-network prediction with noisy predictors

IEEE Transactions on Neural Networks
A hybrid linear-neural model for time series forecasting

IEEE Transactions on Neural Networks
Prediction of noisy chaotic time series using an optimal radial basis function neural network

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.