Adaptive Neural Networks Control on Ship's Linear-Path Following

被引：0

作者：

Li, Wei ^{[1
]}

Ning, Jun ^{[1
]}

Liu, Zhengjiang ^{[1
]}

Li, Tieshan ^{[1
]}

机构：

[1] Dalian Maritime Univ, Nav Coll, Dalian, Peoples R China

来源：

NEURAL INFORMATION PROCESSING, ICONIP 2012, PT V | 2012年 / 7667卷

关键词：

RBF Neural Networks; DSC; Ship's Linear-Tracking Control; Backstepping; NONLINEAR-SYSTEMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we investigate the problem of linear tracking control for an underactuated surface ship with rudder actuator dynamics. By using Radial Basis Function(RBF) Neural Networks(NN) to approximate the uncertainties of the systems, the problem of singularity is avoided and the trouble caused by "explosion of complexity" in traditional backstepping methods is removed by taking advantage of dynamic surface control (DSC) technique. Also, it is proved that all the signals of the closed-loop system are uniformly ultimately bounded(UUB), and the tracking error converges to the neighborhood of zero. The simulation results on an ocean-going training ship 'YULONG' are shown to validate the proposed algorithm.

引用

页码：418 / 427

页数：10

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