On-Line Modular Identification Based on Recursive PLS Regression with Application to Predictive Ship Motion Control

被引：0
|
作者：
Yin Jian-chuan [1 ,2 ]
Zou Zao-jian [1 ,3 ]
Xu Feng [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Shanghai 200240, Peoples R China
[2] Dalian Maritime Univ, Nav Coll, Dalian 116026, Liaoning, Peoples R China
[3] Shanghai Jiao Tong Univ, State Key Lab Ocean Engn, Shanghai 200240, Peoples R China
来源：
PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE | 2012年
基金：
中国国家自然科学基金;
关键词：
Modular predictor; Recursive partial least squares; Ship motion control; NEURAL-NETWORK; MODEL;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An on-line modular predictor is proposed for control application of ship maneuvering motion. This approach combines parametric identification with non-parametric identifications, which are realized based upon recursive partial least squares regression and variable neural network, respectively. Simulation of predictive ship course control is performed by employing the modular predictor for on-line motion prediction. Simulations results of ship motion prediction and control demonstrate the feasibility and effectiveness of the proposed modular predictor.
引用
收藏
页码：4562 / 4567
页数：6
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