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.

本文研究了人工神经网络在船舶航向控制系统中的应用。提出了两层前馈神经网络课程控制系统。第一个神经网络扮演着舰前动态逼近器的角色。第二个是课程控制器。使用从系统功能过程中获取的训练数据，在准在线状态下训练两个神经网络，以应对不断变化的船舶动力。成本函数用于控制动作计算。所提出系统的性能在不同条件下进行评估。通过仿真验证了系统的稳定性。