Fully coupled time-domain simulation of dynamic positioning semi-submersible platform using dynamic surface control

被引:0
作者
Haizhi Liang
Luyu Li
Jinping Ou
机构
[1] Dalian University of Technology,Department of Engineering Mechanics
[2] Dalian University of Technology,Faculty of Infrastructure Engineering
来源
Journal of Ocean University of China | 2014年 / 13卷
关键词
dynamic positioning system; coupled analysis; dynamic surface control; RBF NNs; adaptive control;
D O I
暂无
中图分类号
学科分类号
摘要
A fully coupled 6-degree-of-freedom nonlinear dynamic model is presented to analyze the dynamic response of a semi-submersible platform which is equipped with the dynamic positioning (DP) system. In the control force design, a dynamic model of reference linear drift frequency in the horizontal plane is introduced. The dynamic surface control (DSC) is used to design a control strategy for the DP. Compared with the traditional back-stepping methods, the dynamic surface control combined with radial basis function (RBF) neural networks (NNs) can avoid differentiating intermediate variables repeatedly in every design step due to the introduction of a first order filter. Low frequency motions obtained from total motions by a low pass filter are chosen to be the inputs for the RBF NNs which are used to approximate the low frequency wave force. Considering the propellers’ wear and tear, the effect of filtering frequencies for the control force is discussed. Based on power consumptions and positioning requirements, the NN centers are determined. Moreover, the RBF NNs used to approximate the total wave force are built to monitor the disturbances. With the DP assistance, the results of fully coupled dynamic response simulations are given to illustrate the effectiveness of the proposed control strategy.
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页码:407 / 414
页数:7
相关论文
共 31 条
[1]  
Balchen J G(1980)A dynamic positioning system based on Kalman filtering and optimal control model Modeling, Identification and Control 1 135-163
[2]  
Jenseen N A(1998)Nonlinear output feedback control of dynamically positioned ships using vectorial observer backstepping IEEE Transactions on Control systems Technology 6 121-128
[3]  
Mathisen E(2004)Robust and adaptive back-stepping control for nonlinear systems using RBF neural networks IEEE Transactions on Neural Networks 15 693-701
[4]  
Saelid S(2005)Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory Automatica 41 289-298
[5]  
Fossen T I(2004)A nonlinear ship maneuvering model: Identification and adaptive control with experiment for a model ship Modeling, Identification and Control 25 3-27
[6]  
Grøvlen A(2011)A survey of dynamic positioning control systems Annual Reviews in Control 35 123-136
[7]  
Li Y H(1996)Design of a dynamic positioning system using model-based control Control Engineering Practice 4 359-368
[8]  
Qiang S(2010)Dynamic positioning systems: An experimental analysis of sliding model control Control Engineering Practice 18 1121-1132
[9]  
Zhuang X Y(2006)Adaptive control strategy for the dynamic positioning of a shuttle tanker during offloading operations Journal of Offshore Mechanics and Arctic Engineering 128 203-210
[10]  
Kaynak O(2005)Neural networks-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form IEEE Transactions on Neural Networks 16 195-202