Volume 24 Issue 3
Jun.  2024
Turn off MathJax
Article Contents
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

Modeling and prediction method of ship maneuvering motion facing environmental uncertainty

doi: 10.19818/j.cnki.1671-1637.2024.03.020
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
  • 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.

     

  • loading
  • [1]
    YAN Xin-ping, HE Ya-peng, HE Yi, et al. Development trends of waterway transportation technology[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 1-9. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2022.04.001
    [2]
    ZHAO Bai-gang, ZHANG Xian-ku, LI Zheng, et al. Research on ship motion identification modeling[J]. Ship Science and Technology, 2021, 43(23): 21-24. (in Chinese) doi: 10.3404/j.issn.1672-7649.2021.12.004
    [3]
    ZHANG Yan-yun, WANG Zi-hao, ZOU Zao-jian. Black-box modeling of ship maneuvering motion based on multi-output nu-support vector regression with random excitation signal[J]. Ocean Engineering, 2022, 257: 111279. doi: 10.1016/j.oceaneng.2022.111279
    [4]
    ZHANG Xiu-feng, WANG Xiao-xue, MENG Yao, et al. Research progress and future development trend of ship motion modeling and simulation[J]. Journal of Dalian Maritime University, 2021, 47(1): 1-8. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLHS202101001.htm
    [5]
    LU Guan-yu, YAO Jian-xi. Black-box modeling of ship maneuvering by means of SVR[J]. Navigation of China, 2021, 44(4): 13-19, 31. (in Chinese) doi: 10.3969/j.issn.1000-4653.2021.04.003
    [6]
    MEI Bin, SUN Li-cheng, SHI Guo-you, et al. Grey box identification modeling for ship maneuverability based on single parameter self-adjustable RM-GO-LSVR[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 88-99. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.02.008
    [7]
    LUO Wei-lin, ZOU Zao-jian. Identification of response models of ship maneuvering motion using support vector machines[J]. Journal of Ship Mechanics, 2007, 11(6): 832-838. doi: 10.3969/j.issn.1007-7294.2007.06.003
    [8]
    WANG Xue-gang, ZOU Zao-jian, YU Long, et al. System identification modeling of ship manoeuvring motion in 4 degrees of freedom based on support vector machines[J]. China Ocean Engineering, 2015, 29(4): 519-534. doi: 10.1007/s13344-015-0036-9
    [9]
    CHEN Li-jia, YANG Pei-yi, LI Sheng-wei, et al. Grey-box identification modeling of ship maneuvering motion based on LS-SVM[J]. Ocean Engineering, 2022, 266: 112957. doi: 10.1016/j.oceaneng.2022.112957
    [10]
    XU Feng, LIU Zhi-ping, ZHENG Hai-bin, et al. On-line modeling of ship maneuvering motion by using LS-SVM[J]. Journal of Ship Mechanics, 2021, 25(6): 752-759. (in Chinese) doi: 10.3969/j.issn.1007-7294.2021.06.007
    [11]
    XIE Shuo, CHU Xiu-min, LIU Chen-guang, et al. Online parameter identification method for ship maneuvering models based on improved LSSVM[J]. Shipbuilding of China, 2018, 59(2): 178-189. (in Chinese) doi: 10.3969/j.issn.1000-4882.2018.02.019
    [12]
    QIU Wen-qin, TANG Cun-bao, TANG Qiang-rong. Navigation environment risk assessment of uncertain inland waterway[J]. Navigation of China, 2019, 42(1): 52-55, 67. (in Chinese) doi: 10.3969/j.issn.1000-4653.2019.01.011
    [13]
    FAN Cun-long, ZHANG Di, YAO Hou-jie, et al. Navigational risk identification for maritime autonomous surface ship[J]. Navigation of China, 2019, 42(2): 75-82. (in Chinese) doi: 10.3969/j.issn.1000-4653.2019.02.015
    [14]
    HUANG Bai-gang, ZOU Zao-jian. Online prediction of ship roll motion in irregular waves using a fixed grid wavelet network[J]. Journal of Ship Mechanics, 2020, 24(6): 693-705. (in Chinese) doi: 10.3969/j.issn.1007-7294.2020.06.001
    [15]
    WANG Tong-tong, LI Guo-yuan, WU Bai-hang, et al. Parameter identification of ship manoeuvrling model under disturbance using support vector machine method[J]. Ships and Offshore Structures, 2021, 16(S1): 13-21.
    [16]
    XIA Zhao-dan, MA Xiang, LIU Yi, et al. Machine learning for ship maneuvering in wave[J]. Chinese Journal of Hydrodynamics, 2022, 37(6): 763-768. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SDLJ202206003.htm
    [17]
    JIANG Yan, HOU Xian-rui, WANG Xue-gang, et al. Identification modeling and prediction of ship maneuvering motion based on LSTM deep neural network[J]. Journal of Marine Science and Technology, 2022, 27(1): 125-137. doi: 10.1007/s00773-021-00819-9
    [18]
    XUE Yi-fan, CHEN Gang, LI Zhi-tong, et al. Online identification of a ship maneuvering model using a fast noisy input Gaussian process[J]. Ocean Engineering, 2022, 250: 110704. doi: 10.1016/j.oceaneng.2022.110704
    [19]
    JIANG Yan, WANG Xue-gang, HOU Xian-rui, et al. Comparative study on identification of ship maneuvering motion based on deep recurrent neural network[J]. Journal of Marine Science and Technology, 2023, 38(2): 187-194. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SDLJ202302004.htm
    [20]
    ZHOU Xiao, ZOU Lu, OUYANG Zi-lu, et al. Nonparametric modeling of ship maneuvering motions in calm water and regular waves based on R-LSTM hybrid method[J]. Ocean Engineering, 2023, 285: 115259. doi: 10.1016/j.oceaneng.2023.115259
    [21]
    ZENG Dao-hui, CAI Cheng-tao. Identification of the ship maneuvering response model based on recursive refined instrumental variable least-squares[J]. Journal of Harbin Engineering University, 2023, 44(2): 161-171. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG202302001.htm
    [22]
    ZENG Ke, GU Min, LU Jiang, et al. Numerical prediction of parametric rolling of ships in cross waves[J]. Shipbuilding of China, 2021, 62(4): 65-74. (in Chinese) doi: 10.3969/j.issn.1000-4882.2021.04.005
    [23]
    SON K, NOMOTO K. On the coupled motion of steering and rolling of a high speed container ship[J]. Journal of the Society of Naval Architects of Japan, 1981, 1981(150): 232-244. doi: 10.2534/jjasnaoe1968.1981.150_232
    [24]
    HU Yi, SONG Li-fei, LIU Zu-yuan, et al. Identification of ship hydrodynamic derivatives based on LS-SVM with wavelet threshold denoising[J]. Journal of Marine Science and Engineering, 2021, 9(12): 1356-1356. doi: 10.3390/jmse9121356
    [25]
    CHEN Li-jia, YANG Pei-yi, LI Shi-gang, et al. Online modeling and prediction of maritime autonomous surface ship maneuvering motion under ocean waves[J]. Ocean Engineering, 2023, 276: 114183. doi: 10.1016/j.oceaneng.2023.114183
    [26]
    NIE Zhi-hong, SHEN Feng, XU Ding-jie, et al. An EMD-SVR model for short-term prediction of ship motion using mirror symmetry and SVR algorithms to eliminate EMD boundary effect[J]. Ocean Engineering, 2020, 217: 107927. doi: 10.1016/j.oceaneng.2020.107927
    [27]
    MENG Yao, ZHANG Xiu-feng, CHEN Yu-nong. Parameter identification of a ship mathematical model based on the modified grey wolf algorithm[J]. Journal of Harbin Engineering University, 2023, 44(8): 1304-1312. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG202308009.htm
    [28]
    HOU Xian-rui. Identification modeling of ship motions in waves based on support vector regression[D]. Shanghai: Shanghai Jiao Tong University, 2017. (in Chinese)
    [29]
    ZHANG Xin-guang. Online identification modeling of ship manoeuvring motion using support vector regression[J]. Ship Engineering, 2019, 41(3): 98-101. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CANB201903022.htm
    [30]
    WANG Zi-hao, XU Hai-tong, XIA Li, et al. Kernel-based support vector regression for nonparametric modeling of ship maneuvering motion[J]. Ocean Engineering, 2020, 216: 107994. doi: 10.1016/j.oceaneng.2020.107994
    [31]
    SHEN Wen-he, YAO Jiang-xi, HU Xin-jue, et al. Ship dynamics model identification based on semblance least square support vector machine[J]. Ocean Engineering, 2023, 287: 115908. doi: 10.1016/j.oceaneng.2023.115908
    [32]
    DONG Lei, MA Xiang, FENG Jia-xiang, et al. Online prediction method of ship maneuvering motion based on improved long-short term memory neural network[J]. Shipbuilding of China, 2023, 64(2): 184-198. (in Chinese) doi: 10.3969/j.issn.1000-4882.2023.02.017
    [33]
    LUO Wei-lin, SOARES C G, ZOU Zao-jian. Parameter identification of ship maneuvering model based on support vector machines and particle swarm optimization[J]. Journal of Offshore Mechanics and Arctic Engineering, 2016, 138(3): 031101. doi: 10.1115/1.4032892
    [34]
    LIU Chang-de, GU Yu-xiang, ZHANG Jin-feng. Extreme short-term prediction of ship motions based on wavelet filter and LSTM neural network[J]. Journal of Ship Mechanics, 2021, 25(3): 299-310. (in Chinese) doi: 10.3969/j.issn.1007-7294.2021.03.005
    [35]
    PARAND K, AGHAEI A A, JANI M, et al. Parallel LS-SVM for the numerical simulation of fractional Volterra's population model[J]. Alexandria Engineering Journal, 2021, 60(6): 5637-5647. doi: 10.1016/j.aej.2021.04.034
    [36]
    SHEN Wen-jun, ZHAO Zhi-juan, LIU Li-qin, et al. Research of wave period effect on the dynamic response characteristics of a small ship[J]. Journal of Ship Mechanics, 2022, 26(3): 342-352. (in Chinese) doi: 10.3969/j.issn.1007-7294.2022.03.004
    [37]
    ZHANG Teng, REN Jun-sheng, MEI Tian-long. Mathematical model of ship motions in regular waves based on Froude-Krylov force nonlinear method[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 77-87. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.02.007
    [38]
    QIAN Xiao-bin, YIN Yong, ZHANG Xiu-feng, et al. Influence of irregular disturbance of sea wave on ship motion[J]. Journal of Traffic and Transportation Engineering, 2016, 16(3): 116-124. (in Chinese) doi: 10.3969/j.issn.1671-1637.2016.03.014
    [39]
    JIANG Hong, SUN Shi-wei, WEI Chang-jin, et al. Design of intelligent shipping ship-shore communication system and optimization of network data transmission[J]. Ship Science and Technology, 2020, 42(20): 178-180. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JCKX202020061.htm
    [40]
    SUTULO S, GUEDES SOARES C. An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests[J]. Ocean Engineering, 2014, 79: 10-25. doi: 10.1016/j.oceaneng.2014.01.007

Catalog

    Article Metrics

    Article views (377) PDF downloads(46) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return