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Research Papers: Offshore Technology

Parametric Identification of Abkowitz Model for Ship Maneuvering Motion by Using Partial Least Squares Regression

[+] Author and Article Information
Yin Jian-Chuan

School of Naval Architecture,
Ocean and Civil Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
Navigation College,
Dalian Maritime University,
Dalian 116026, Liaoning, China

Zou Zao-Jian

School of Naval Architecture,
Ocean and Civil Engineering;
State Key Laboratory of Ocean Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: zjzou@sjtu.edu.cn

Xu Feng

School of Naval Architecture,
Ocean and Civil Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China

1Corresponding author

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received July 20, 2012; final manuscript received January 23, 2015; published online March 10, 2015. Editor: Solomon Yim.

J. Offshore Mech. Arct. Eng 137(3), 031301 (Jun 01, 2015) (8 pages) Paper No: OMAE-12-1076; doi: 10.1115/1.4029827 History: Received July 20, 2012; Revised January 23, 2015; Online March 10, 2015

Partial least squares (PLS) regression is used for identifying the hydrodynamic derivatives in the Abkowitz model for ship maneuvering motion. To identify the dynamic characteristics in ship maneuvering motion, the derivatives of hydrodynamic model's outputs are set as the target output of the PLS identification model. To verify the effectiveness of PLS parametric identification method in processing data with high dimensionality and heavy multicollinearity, the identified results of the hydrodynamic derivatives from the simulated 20 deg/20 deg zigzag test are compared with the planar motion mechanism (PMM) test results. The performance of PLS regression is also compared with that of the conventional least squares (LS) regression using the same dataset. Simulation results show the satisfactory identification and generalization performances of PLS regression and its superiority in comparison with the LS method, which demonstrates its capability in processing measurement data with high dimensionality and heavy multicollinearity, especially in processing data with small sample size.

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Figures

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Fig. 1

Simulation results of 20 deg/20 deg zigzag test

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Fig. 2

Identification and generalization performances of Δu· with different number of samples: (a) PLS regression and (b) LS regression

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Fig. 3

Identification and generalization performances of Δv· with different number of samples: (a) PLS regression and (b) LS regression

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Fig. 4

Identification and generalization performances of Δr· with different number of samples: (a) PLS regression and (b) LS regression

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Fig. 5

Generalization error of Δu· with different number of samples: (a) PLS regression and (b) LS regression

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Fig. 6

Generalization error of Δv· with different number of samples: (a) PLS regression and (b) LS regression

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Fig. 7

Generalization error of Δr· with different number of samples: (a) PLS regression and (b) LS regression

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Fig. 8

Training data of 20 deg/20 deg zigzag test (with 10% noise)

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Fig. 9

Identification (with noise) and generalization (noise-free) performance for Δu·: (a) PLS regression and (b) LS regression

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Fig. 10

Identification (with noise) and generalization (noise-free) performance for Δv·: (a) PLS regression and (b) LS regression

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Fig. 11

Identification (with noise) and generalization (noise-free) performance for Δr·: (a) PLS regression and (b) LS regression

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