【摘要】 针对大型船舶的航向控制特性,提出一种神经网络并行自学习鲁棒模型参考控制方法。这种复合控制结构利用神经网络的学习能力和非线性映射能力,解决传统自适应控制中模型的在线辨识和控制器的在线设计问题,以达到对不确定非线性船舶的高精度输出跟踪控制;通过引入运行监控器,克服神经网络控制方法实时性差的问题;利用一个鲁棒反馈控制器,来保证神经网络模型学习初期闭环系统的稳定性。仿真结果表明这一方法对设定航向具有精确的跟踪控制效果。更多还原

【Abstract】 Aiming at the control feature of large ship, a neural network based parallel self- learning robust model-reference control method of ship’s course was proposed. This compounded control structure using the self-learning and nonlinear map-capability of neural networks, solved problems of online model-identification and online design of the controller in traditional adaptive control, so that the high precise output-track control of uncertain nonlinear large ship could be realized. A robust feedback-controller was imported to ensure closed-loop stability in the initial learning stages of NN model and improve the NN control’s real-time ability. Simulation results showed that the method had perfect control effect.更多还原