Ship heading control system using neural network

Abstract

In this paper, the application of artificial neural network in ship course control systems is investigated. Two-multilayered feed-forward neural network course control system is proposed. The first neural network plays the role of ship forward dynamic approximator. The second one is the course controller. Both neural networks are trained in a quasi-online regime using training data acquired from system functional process to cope with changing ship dynamics. A cost function is used in control action calculation. The performance of the proposed system is evaluated in different conditions. The system stability is verified via simulation. The simulation results show that the course control system is able to keep the predefined direction in various sea conditions and the proposed approach serves the consideration on developing and applying in designing real ship autopilot systems.