Modeling and prediction method of ship maneuvering motion facing environmental uncertainty

CHEN Li-jia; ZHOU Xin-wei; YANG Pei-yi; WANG Kai; LI Sheng-wei

doi:10.19818/j.cnki.1671-1637.2024.03.020

Volume 24 Issue 3

Jun. 2024

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Article Navigation > Journal of Traffic and Transportation Engineering > 2024 > 24(3): 279-295

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CHEN Li-jia, ZHOU Xin-wei, YANG Pei-yi, WANG Kai, LI Sheng-wei. Modeling and prediction method of ship maneuvering motion facing environmental uncertainty[J]. Journal of Traffic and Transportation Engineering, 2024, 24(3): 279-295. doi: 10.19818/j.cnki.1671-1637.2024.03.020

Citation:

CHEN Li-jia, ZHOU Xin-wei, YANG Pei-yi, WANG Kai, LI Sheng-wei. Modeling and prediction method of ship maneuvering motion facing environmental uncertainty[J]. Journal of Traffic and Transportation Engineering, 2024, 24(3): 279-295. doi: 10.19818/j.cnki.1671-1637.2024.03.020

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Modeling and prediction method of ship maneuvering motion facing environmental uncertainty

doi: 10.19818/j.cnki.1671-1637.2024.03.020

CHEN Li-jia^{1, 2, 3
,},
ZHOU Xin-wei^{1, 2, 3},
YANG Pei-yi^{1, 2, 3},
WANG Kai^{1, 2, 3},
LI Sheng-wei^{1, 2, 3}

1.
School of Navigation, Wuhan University of Technology, Wuhan 430063, Hubei, China
2.
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, Hubei, China
3.
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, Hubei, China

Funds:

National Key Research and Development Program of China 2019YFB1600603

More Information

Author Bio:
CHEN Li-jia(1979-), male, associate professor, PhD, navisky@qq.com
Received Date: 2024-01-09
Available Online: 2024-07-18
Publish Date: 2024-06-30

Abstract

Abstract

In response to the issue of prediction accuracy of ship maneuvering motion under complicated environmental factors, a grey box identification modeling and prediction method for ship maneuvering motion under environmental uncertainty was proposed. The separated ship maneuvering motion model structure was referenced, the ship maneuvering motion mechanism was considered, and a simplified grey box model was developed. Suitable test subjects were selected, and parameter identification was conducted on the established ship maneuvering motion grey box model using the least squares support vector machine algorithm. The generalization ability was examined by means of the turning cycle tests and zigzag maneuvering tests. By analyzing the environmental uncertainty factors, the wave force interference model, data transmission delay model, and sensing device error model were constructed. Based on these models, the ship motion response training data affected by multiple environmental uncertainties were generated. Through the simulated tests, the prediction accuracy of the proposed method under environmental uncertainties was validated. Research results reveal that in ship maneuvering motion prediction tests with environmental uncertainty factors, when the sensing device error gradually increases from 0 to 5% and 10%, except for the rolling speed affected by a small initial magnitude, the root mean square errors (RMSEs) of other ship motion response prediction results increase by less than 10%, so the accuracy of the prediction model can be effectively guaranteed. Under the extreme condition with a 20% sensing device error, the prediction errors of surge speed, sway speed, and yawing speed increase by 4.65%, 15.97%, and 18.17%, respectively compared to the 0 error level, so the error increase is effectively controlled below 20%. Thus, the ship maneuvering motion modeling and prediction method can achieve a high-precision prediction of ship maneuvering motion under the interference of environmental uncertainty factors to a certain extent.
- water transport,
- ship maneuvering motion,
- environmental uncertainty,
- LS-SVM,
- modeling and prediction method,
- verification test

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References(40)

References

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Modeling and prediction method of ship maneuvering motion facing environmental uncertainty

doi: 10.19818/j.cnki.1671-1637.2024.03.020

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