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。
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.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2007年第15期3489-3493,共5页
Journal of System Simulation
基金
This work was supported in part by the National Natural Science Foundation of China (60474014)
in part by the Specialized Research Fund for the Doctoral Program of Higher Education of P.R.C. under grant (20040151007)
in part by the Application foundation Research Project of Ministry of Communications of P.R.C. under grant (200432922504)
关键词
神经网络
船舶运动控制
模型参考控制
非线性控制
仿真
neural network
ship motion control
model reference control
nonlinear control
simulation
