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ZHU Man, WEN Yuanqiao, SUN Wuqiang, ZHANG Jiahui, HAHN Axel. A Review of Parameter Identification Methods for Ship Dynamic Models[J]. Journal of Transport Information and Safety, 2022, 40(5): 1-11. doi: 10.3963/j.jssn.1674-4861.2022.05.001
Citation: ZHU Man, WEN Yuanqiao, SUN Wuqiang, ZHANG Jiahui, HAHN Axel. A Review of Parameter Identification Methods for Ship Dynamic Models[J]. Journal of Transport Information and Safety, 2022, 40(5): 1-11. doi: 10.3963/j.jssn.1674-4861.2022.05.001
shu

A Review of Parameter Identification Methods for Ship Dynamic Models

doi: 10.3963/j.jssn.1674-4861.2022.05.001
  • 1.

    Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

  • 2.

    Sanya Science and Education Innovation Park of Wuhan University of Technology, Sanya 572025, Hainan, China

  • 3.

    National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China

  • 4.

    Institute of Artificial Intelligent, Sunwin Intelligent Co., Ltd., Hefei 230000, China

  • 5.

    School of Navigation, Wuhan University of Technology, Wuhan 430063, China

  • 6.

    Computer Science, Carl-von-Ossietzky University of Oldenburg, Oldenburg 26121, Germany

  • Received Date: 2022-04-22
    Available Online: 2022-12-05
    Abstract
  • Developing a dynamic model with a high accuracy and reliability is essential for analyzing ship maneuverability and ensuring shipping safety. Compared with the existing three popular methods, including empirical, experimental and computational fluid dynamics (CFD), numerical parameter identification methods are practical, effective, and powerful solutions for dynamic modeling. However, it faces major challenges due to the influences from the following factors, such as strong nonlinear motion characteristics of ships, varying environmental interfer-ences, and the multiple internal and external uncertainties. This paper reviews the state-of-the-art of parameter identification of ship dynamic models from the following four critical perspectives, including the optimal input design related to the informative characteristics acquisition, the mathematical model of ship motion, parameter estimation algorithms, and the verification and validation of the identified models. Several critical problems are discussed including noise interference, parameter drift, parameter variation, and selection of evaluation indexes. Based on a comprehensive survey, two challenging issues are pointed out. Currently, there is no method available that can provide high-quality data covering motion characteristics over a wide area, and due to the complexity of the models, the parameter drift fluctuates and cannot be completely avoided. Potential research questions closely related to the parameter identification of ship dynamic models are discussed. For instance, ship dynamic data acquired and processed using robust estimation techniques or information fusion techniques is worthy to be addressed to provide high-quality data; robust online parameter identification based on the multi-innovation intelligent approach can be a valuable solution to real-time identification of ship dynamic models; and complex conditions, such as ships sailing in restricted waters, should to be examined.

     

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    • References(55)
  • [1]
    KOSOWATZ J. Sailing toward autonomy[J]. Mechanical Engineering, 2019, 141(9): 30-35. doi: 10.1115/1.2019-SEP1
    [2]
    吴青, 王乐, 刘佳仑. 自主水面货船研究现状与展望[J]. 智能系统学报, 2019, 14(1): 57-70. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNXT201901005.htm

    WU Q, WANG L, LIU J L. Research status and prospects of autonomous surface cargo ships[J]. CAAI Transactions on Intelligent Systems, 2019, 14(1): 57-70. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZNXT201901005.htm
    [3]
    周翔宇, 吴兆麟, 王凤武, 等. 自主船舶的定义及其自主水平的界定[J]. 交通运输工程学报, 2019, 19(6): 149-162. doi: 10.19818/j.cnki.1671-1637.2019.06.014

    ZHOU X Y, WU Z L, WANG F W, et al. Definition of autonomous ship and its autonomy level[J]. Journal of Traffic and Transportation Engineering, 2019, 19(6): 149-162. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2019.06.014
    [4]
    张笛, 赵银祥, 崔一帆, 等. 智能船舶的研究现状可视化分析与发展趋势[J]. 交通信息与安全, 2021, 39(1): 7-16+34.

    ZHANG D, ZHAO Y X, CUI Y F, et al. A visualization analysis and development trend of intelligent ship studies[J]. 2021, 39(1): 7-16+34. (in Chinese)
    [5]
    王雪刚. 基于支持向量机的四自由度船舶操纵运动建模研究[D]. 上海: 上海交通大学, 2014.

    WANG X G. On the modeling of ship manoeuvring motion in 4 degree of freedom based on support vector machines[D]. Shanghai: Shanghai Jiaotong University, 2014. (in Chinese)
    [6]
    ZHU M, HAHN A, WEN Y Q, et al. Optimized support vector regression algorithm-based modeling of ship dynamics[J]. Applied Ocean Research, 2019(90): 101842.
    [7]
    王辉, 任俊生, 刘新召. 基于OpenMP的船舶操纵运动局部加权学习辨识建模[J]. 计算机应用研究, 2020, 37(增刊2): 173-175. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ2020S2054.htm

    WANG H, REN J S, LIU X Z. OpenMP-based local weighted learning identification modeling of ship maneuvering motion[J]. Application Research of Computers, 2020, 37(S2): 173-175. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ2020S2054.htm
    [8]
    LJUNG L. System identification[M]. Boston: Birkhäuser, 1998.
    [9]
    WANG Z, SOARES CARLOS G, ZOU Z. Optimal design of excitation signal for identification of nonlinear ship manoeuvring model[J]. Ocean Engineering, 2019(196): 106778.
    [10]
    郑宇昕. 最优输入设计方法及其在飞行器参数辨识中的应用[D]. 长沙: 国防科技大学, 2018.

    ZHENG X Y. Optimal input design method and aircraft parameter identification[D]. Changsha: National University of Defense Technology, 2018. (in Chinese)
    [11]
    LEVIN M J. Optimal estimation of impulse response in the presence of noise[J]. IRE Transactions on Circuit Theory, 1960(7): 50-56.
    [12]
    XU H, HINOSTROZA M A, SOARES CARLOS G. Estimation of hydrodynamic coefficients of a nonlinear manoeuvring mathematical model with free-running ship model tests[J]. International Journal of Maritime Engineering, 2018(160): 213-225.
    [13]
    ZHU M, SUN W Q, HAHN A, et al. Adaptive modeling of maritime autonomous surface ships with uncertainty using a weighted LS-SVR robust to outliers[J]. Ocean Engineering, 2020(200): 107053.
    [14]
    PERERA L P, OLIVEIRA P, SOARES CARLOS G. System identification of vessel steering with unstructured uncertainties by persistent excitation maneuvers[J]. IEEE Journal of Oceanic Engineering, 2015, 41(3): 515-528.
    [15]
    HERRERO E R, GONZALEZ F J V. Two-step identification of non-linear manoeuvring models of marine vessels[J]. Ocean Engineering, 2012(53): 72-82.
    [16]
    WANG Z, ZOU Z, SOARES CARLOS G. Identification of ship manoeuvring motion based on nu-support vector machine[J]. Ocean Engineering, 2019(183): 270-281.
    [17]
    YOON H K, RHEE K P. Identification of hydrodynamic coefficients in ship maneuvering equations of motion by estimation-before-modeling technique[J]. Ocean Engineering, 2003, 30(18): 2379-2404. doi: 10.1016/S0029-8018(03)00106-9
    [18]
    YEON S M, YEO D J, RHEE K P. Optimal input design for the identification of low-speed manoeuvring mathematical model[C]. International Conference on Marine Simulation and Ship Maneuverability(MARSIM2006), Terschelling, The Netherlands: MIWB, 2006.
    [19]
    SUTULO S, SOARES CARLOS G. Development of a multifactor regression model of ship maneuvering forces based on optimized captive-model tests[J]. Journal of Ship Research, 2006, 50(4): 311-333. doi: 10.5957/jsr.2006.50.4.311
    [20]
    LEWIS E V. Principles of naval architecture second revision[J]. Jersey: SNAME, 1988(2): 152-157.
    [21]
    YASUKAWA H, YOSHIMURA Y. Introduction of MMG standard method for ship maneuvering predictions[J]. Journal of Marine Science and Technology, 2015, 20(1): 37-52. doi: 10.1007/s00773-014-0293-y
    [22]
    FOSSEN T I. Handbook of marine craft hydrodynamics and motion control[M]. Manhattan: John Wiley & Sons, 2011.
    [23]
    TZENG C Y, CHEN J F. Fundamental properties of linear ship steering dynamic models[J]. Journal of Marine Science and Technology, 1999, 7(2): 79-88.
    [24]
    LIU J, HEKKENBERG R, QUADVLIEG F, et al. An integrated empirical manoeuvring model for inland vessels[J]. Ocean Engineering, 2017(137): 287-308.
    [25]
    SUTULO S, SOARES CARLOS G. On the application of empiric methods for prediction of ship manoeuvring properties and associated uncertainties[J]. Ocean Engineering, 2019(186): 106111.
    [26]
    XU H, SOARES CARLOS G. Hydrodynamic coefficient estimation for ship manoeuvring in shallow water using an optimal truncated LS-SVM[J]. Ocean Engineering, 2019(191): 106488.
    [27]
    DU P, OUAHSINE A, SERGENT P. Influences of the separation distance, ship speed and channel dimension on ship maneuverability in a confined waterway[J]. Comptes Rendus Mécanique, 2018, 346(5): 390-401. doi: 10.1016/j.crme.2018.01.005
    [28]
    TANG X, TONG S, HUANG G, et al. Numerical investigation of the maneuverability of ships advancing in the non-uniform flow and shallow water areas[J]. Ocean Engineering, 2020(195): 106679.
    [29]
    刘晗. 船舶近岸壁航行操纵性水动力与运动稳定性研究[D]. 上海: 上海交通大学, 2017.

    LIU H. Manoeuvring hydrodynamics and stability of vessels navigating in proximity to the bank[D]. Shanghai: Shanghai Jiaotong University, 2017. (in Chinese)
    [30]
    FALTINSEN O M. Hydrodynamics of high-speed marine vehicles[M]. Cambridge, UK: Cambridge University Press, 2005.
    [31]
    ZHU M. Optimized support vector regression algorithm-based modeling of ship dynamic[D]. Oldenburg: Oldenburg University, 2019.
    [32]
    MIYAUCHI Y, MAKI A, UMEDA N, et al. System parameter exploration of ship maneuvering model for automatic docking/berthing using CMA-ES[J]. Journal of Marine Science and Technology, 2022, 27(2): 1065-1083. doi: 10.1007/s00773-022-00889-3
    [33]
    张心光. 基于船舶操纵性试验分析的辨识建模研究[D]. 上海: 上海交通大学, 2012.

    ZHANG X G. Identification modeling based on analysis of ship manoeuvring tests[D]. Shanghai: Shanghai Jiaotong University, 2012. (in Chinese)
    [34]
    CHEN C, RUIZ M T, DELEFORTRIE G, et al. Parameter estimation for a ship's roll response model in shallow water using an intelligent machine learning method[J]. Ocean Engineering, 2019(191): 106479.
    [35]
    SUN L P, SUN W B. Parameter identification of the non-linear rolling damping based on PLS regression technique[J]. Advanced Materials Research, 2013(779/780): 675-679.
    [36]
    谢朔, 初秀民, 柳晨光, 等. 基于多新息最小二乘法的船舶操纵响应模型参数辨识[J]. 中国航海, 2017, 40(1): 73-78. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201701016.htm

    XIE S, CHU X M, LIU C G, et al. Parameter identification of ship maneuvering response model based on multi-innovation least squares algorithm[J]. Navigation of China, 2017, 40(1): 73-78. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201701016.htm
    [37]
    SONG C, ZHANG X, ZHANG G. Nonlinear identification for 4-DOF ship maneuvering modeling via full-scale trial data[J]. IEEE Transactions on Industrial Electronics, 2021, 69(2): 1829-1835.
    [38]
    ZHANG G, ZHANG X, PANG H. Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scale trial data[J]. ISA Transactions, 2015(58): 186-195.
    [39]
    HAYES M N. Parametric identification of nonlinear stochastic systems applied to ocean vehicle dynamics[D]. Cambridge, USA: Massachusetts Institute of Technology, 1971.
    [40]
    HWANG W Y. Application of system identification to ship maneuvering[D]. Massachusetts Institute of Technology, 1980.
    [41]
    秦操. 基于无迹卡尔曼滤波的船舶运动数学模型辨识[J]. 舰船科学技术, 2021, 43(1): 89-94. https://www.cnki.com.cn/Article/CJFDTOTAL-JCKX202101017.htm

    QIN C. Parameter identification for ship mathematical model based on unscented Kalman filter[J]. Ship Science and Technology, 2021, 43(1): 89-94. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JCKX202101017.htm
    [42]
    ZHENG J, YAN D, YAN M, et al. An unscented Kalman filter online identification approach for a nonlinear ship motion model using a self-navigation test[J]. Machines, 2022, 10(5): 312-320. doi: 10.3390/machines10050312
    [43]
    WANG S, WANG L, IM N, et al. Real-time parameter identification of ship maneuvering response model based on nonlinear Gaussian filter[J]. Ocean Engineering, 2022(247): 110471.
    [44]
    ARAKI M, SADAT-HOSSEINI H, SANADA Y, et al. Estimating maneuvering coefficients using system identification methods with experimental, system-based, and CFD free-running trial data[J]. Ocean Engineering, 2012(51): 63-84.
    [45]
    WANG N, ER M J, HAN M. Large tanker motion model identification using generalized ellipsoidal basis function-based fuzzy neural networks[J]. IEEE Transactions on Cybernetics, 2015, 45(12): 2732-2743.
    [46]
    LUO W. Parameter identifiability of ship manoeuvring mod-eling using system identification[J]. Mathematical Problems in Engineering, 2016(1): 1-10.
    [47]
    LUO W, LI X. Measures to diminish the parameter drift in the modeling of ship manoeuvring using system identification[J]. Applied Ocean Research, 2017(67): 9-20.
    [48]
    SUTULO S, SOARES CARLOS G. An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests[J]. Ocean Engineering, 2014(79): 10-25.
    [49]
    SAJEDI Y, BOZORG M. Robust estimation of hydrodynamic coefficients of an AUV using Kalman and H∞ filters[J]. Ocean Engineering, 2019(182): 386-394.
    [50]
    XU H, SOARES CARLOS G. Vector field path following for surface marine vessel and parameter identification based on LS-SVM[J]. Ocean Engineering, 2016(113): 151-161.
    [51]
    JIANG Y, WANG X G, ZOU Z J, et al. Identification of coupled response models for ship steering and roll motion using support vector machines[J]. Applied Ocean Research, 2021(110): 102607.
    [52]
    WANG Z, ZOU Z, SOARES CARLOS G. Identification of ship manoeuvring motion based on nu-support vector machine[J]. Ocean Engineering, 2019(183): 270-281.
    [53]
    ISERMANN R, MUENCHHOF M. Identification of dynamic systems: An introduction with applications[M]. Berlin: Springer, 2011
    [54]
    任泽裕, 王振超, 柯尊旺, 等. 多模态数据融合综述[J]. 计算机工程与应用, 2021, 57(18): 49-64. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202118006.htm

    REN Z Y, WANG Z C, KE Z W, et al. Survey of multimodal data fusion[J]. Computer Engineering and Applications, 2021, 57(18): 49-64. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202118006.htm
    [55]
    王文飒, 梁军, 陈龙, 等. 基于深度强化学习的协同式自适应巡航控制[J]. 交通信息与安全, 2019, 37(3): 93-100. doi: 10.3963/j.issn.1674-4861.2019.03.012

    WANG W F, LIANG J, CHEN L, et al. Collaborative adaptive cruise control based on deep reinforcement learning[J]. Journal of Transport Information and Safety, 2019, 37(3): 93-100. (in Chinese) doi: 10.3963/j.issn.1674-4861.2019.03.012
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