PDF
Abstract
A state-of-the-art review is presented of mathematical manoeuvring models for surface ships and parameter estimation methods that have been used to build mathematical manoeuvring models for surface ships. In the first part, the classical manoeuvring models, such as the Abkowitz model, MMG, Nomoto and their revised versions, are revisited and the model structure with the hydrodynamic coefficients is also presented. Then, manoeuvring tests, including both the scaled model tests and sea trials, are introduced with the fact that the test data is critically important to obtain reliable results using parameter estimation methods. In the last part, selected papers published in journals and international conferences are reviewed and the statistical analysis of the manoeuvring models, test data, system identification methods and environmental disturbances used in the paper is presented.
Keywords
Manoeuvring simulation
/
System identification
/
Manoeuvring model
/
Manoeuvring test
Cite this article
Download citation ▾
Haitong Xu, C. Guedes Soares.
Review of System Identification for Manoeuvring Modelling of Marine Surface Ships.
Journal of Marine Science and Application, 2025, 24(3): 459-478 DOI:10.1007/s11804-025-00681-w
| [1] |
AbkowitzMA. Measurement of hydrodynamic characteristics from ship maneuvering trials by system identification. SNAME Trans., 1980, 88: 283-318
|
| [2] |
ArakiM, Sadat-HosseiniH, SanadaY, TanimotoK, UmedaN, SternF. Estimating maneuvering coefficients using system identification methods with experimental, system-based, and CFD free-running trial data. Ocean Eng., 2012, 51: 63-84
|
| [3] |
Ariza RamirezW, LeongZQ, NguyenH, JayasingheSG. Non-parametric dynamic system identification of ships using multi-output Gaussian Processes. Ocean Eng., 2018, 166: 26-36
|
| [4] |
ÅströmKJ, KällströmCG. Identification of ship steering dynamics. Automatica, 1976, 12: 9-22
|
| [5] |
BaiW, RenJ, LiT. Modified genetic optimization-based locally weighted learning identification modeling of ship maneuvering with full scale trial. Futur. Gener. Comput. Syst., 2019, 93: 1036-1045
|
| [6] |
BaiW, RenJ, LiT, ChenCLP. Grid index subspace constructed locally weighted learning identification modeling for high dimensional ship maneuvering system. ISA Trans., 2019, 86: 144-152
|
| [7] |
BanazadehA, GhorbaniMT. Frequency domain identification of the Nomoto model to facilitate Kalman filter estimation and PID heading control of a patrol vessel. Ocean Eng., 2013, 72: 344-355
|
| [8] |
BellJB, TikhonovAN, ArseninVY. Solutions of Ill-Posed Problems. Math. Comput., 1978, 32: 1320-1322
|
| [9] |
BishopCM. Neural networks and their applications. Rev. Sci. Instrum., 1998, 65: 1803
|
| [10] |
CaoJ, ZhuangJ, XuF, YinJ, ZouZ, YuH, XiaoT, YangL. Parametric estimation of ship maneuvering motion with integral sample structure for identification. Appl. Ocean Res., 2015, 52: 212-221
|
| [11] |
CasadoMH, FerreiroR. Identification of the nonlinear ship model parameters based on the turning test trial and the backstepping procedure. Ocean Eng., 2005, 32: 1350-1369
|
| [12] |
ChanTF, HansenPC. Computing truncated singular value decomposition least squares solutions by rank revealing QR-Factorizations. SIAM J. Sci. Stat. Comput., 1990, 11: 519-530
|
| [13] |
ChenC, Tello RuizM, DelefortrieG, MeiT, VantorreM, LataireE. Parameter estimation for a ship’s roll response model in shallow water using an intelligent machine learning method. Ocean Eng., 2019, 191: 106479
|
| [14] |
ChenC, Tello RuizM, LataireE, DelefortrieG, MansuyM, MeiT, VantorreM. Ship Manoeuvring Model Parameter Identification Using Intelligent Machine Learning Method and the Beetle Antennae Search Algorithm. Ocean Engineering, 2019Volume 7B
|
| [15] |
ChenCZ, LiuSY, ZouZJ, ZouL, LiuJZ. Time series prediction of ship maneuvering motion based on dynamic mode decomposition. Ocean Engineering, 2023, 286: 115446
|
| [16] |
ChenL, YangP, LiS, LiuK, WangK, ZhouX. Online modeling and prediction of maritime autonomous surface ship maneuvering motion under ocean waves. Ocean Engineering, 2023, 276: 114183
|
| [17] |
ChenL, YangP, LiS, TianY, LiuG, HaoG. Grey-box identification modeling of ship maneuvering motion based on LS-SVM. Ocean Engineering, 2022, 266: 112957
|
| [18] |
ChislettMS, Strom-TejsenJ. Planar motion mechanism tests and full-scale steering and manoeuvring predictions for a Mariner class vessel. Int. Shipbuild. Prog., 1965, 12: 201-224
|
| [19] |
CortesC, VapnikV. Support-vector networks. Mach. Learn., 1995, 20: 273-297
|
| [20] |
CostaAC, XuH, Guedes SoaresC. Robust parameter estimation of an empirical manoeuvring model using free-running model tests. J. Mar. Sci. Eng., 2021, 9: 1302
|
| [21] |
CraneCLJr. Maneuvering trials of the 278000 DWT ESSO OSAKA in shallow and deep waters. SNAME Trans, 1979, 87: 87-251
|
| [22] |
DelefortrieG, ElootK, LataireE, Van HoydonckW, VantorreM. Captive model tests based 6 DOF shallow water manoeuvring model. Proceedings of 4th MASHCON, Hamburg, Germany, 2016
|
| [23] |
DelefortrieG, VantorreM. 6DOF manoeuvring model of KCS with full roll coupling. Ocean Engineering, 2021, 235: 109327
|
| [24] |
DiezM, SeraniA, CampanaEF, SternF. Time-series forecasting of ships maneuvering in waves via dynamic mode decomposition. Journal of Ocean Engineering and Marine Energy, 2022, 8(4): 471-478
|
| [25] |
DongQ, WangN, SongJ, HaoL, LiuS, HanB, QuK. Math-data integrated prediction model for ship maneuvering motion. Ocean Engineering, 2023, 285: 115255
|
| [26] |
DuP, ChengL, TangZJ, OuahsineA, HuHB, HoarauY. Ship maneuvering prediction based on virtual captive model test and system dynamics approaches. Journal of Hydrodynamics, 2022, 34(2): 259-276
|
| [27] |
ElootKSelection, experimental determination and evaluation of a mathematical model for ship manoeuvring in shallow water, 2006, Ghent, Belgium, Ghent University
|
| [28] |
El MoctarO, LantermannU, ChillcceG. An efficient and accurate approach for zero-frequency added mass for maneuvering simulations in deep and shallow water. Applied Ocean Research, 2022, 126: 103259
|
| [29] |
FangMC, TsaiKY, FangCC. A simplified simulation model of ship navigation for safety and collision avoidance in heavy traffic areas. J. Navig., 2018, 71: 837-860
|
| [30] |
FanY, QiaoS, WangG, ChenS, ZhangH. A modified adaptive Kalman filtering method for maneuvering target tracking of unmanned surface vehicles. Ocean Engineering, 2022, 266: 112890
|
| [31] |
FossenTIHandbook of marine craft hydrodynamics and motion control, 2011, Chichester, UK, John Wiley & Sons, Ltd
|
| [32] |
FossenTI, SagatunSI, SørensenAJ. Identification of dynamically positioned ships. Model. Identif. Control, 1996, 17: 153-165
|
| [33] |
GavrilinS, SteenS. Global sensitivity analysis and repeated identification of a modular maneuvering model of a passenger ferry. Appl. Ocean Res., 2018, 74: 1-10
|
| [34] |
GavrilinS, SteenS. Uncertainty of sea trials results used for validation of ship manoeuvring simulation models. ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering, 2015V007T06A016
|
| [35] |
GoldingB, RossA, FossenTI. Identification of nonlinear viscous damping for marine vessels. 14th IFAC Symposium on System Identification, Newcastle, Australia, 2006332-337
|
| [36] |
GolubGH, HansenPC, O’LearyDP. Tikhonov regularization and total least squares. SIAM J. Matrix Anal. Appl., 1999, 21: 185-194
|
| [37] |
GolubGH, ReinschC. Singular value decomposition and least squares solutions. Numer. Math., 1970, 14: 403-420
|
| [38] |
Guedes SoaresC, FranciscoRA, MoreiraL, LaranjinhaM. Full-scale measurements of the maneuvering capabilities of fast patrol vessels. Argos Class. Mar. Technol., 2004, 41: 7-16
|
| [39] |
Guedes SoaresC, SutuloS, FranciscoRA, SantosFM, MoreiraL. Full-scale measurements of the manoeuvring capabilities of a catamaran. Proceedings of the International Conference on Hydrodynamics of High Speed Craft, 1999, London, RINA: 1-12
|
| [40] |
HansenPCRank-deficient and discrete ill-posed problems, numerical aspects of linear inversion, 1998, Philadelphia, SIAM
|
| [41] |
HaoZ, YuS, YangX, ZhaoF, HuR, LiangY. Online LS-SVM learning for classification problems based on incremental chunk. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 2004, 3173: 558-564
|
| [42] |
HaoL, HanY, ShiC, PanZ. Recurrent neural networks for nonparametric modeling of ship maneuvering motion. International Journal of Naval Architecture and Ocean Engineering, 2022, 14: 100436
|
| [43] |
HassaniV, SørensenAJ, PascoalAM. Adaptive wave filtering for dynamic positioning of marine vessels using maximum likelihood identification: Theory and experiments. IFAC Proceedings Volumes (IFAC-PapersOnline), 2013203-208
|
| [44] |
HeHW, WangZH, ZouZJ, LiuY. Nonparametric modeling of ship maneuvering motion based on self-designed fully connected neural network. Ocean Eng., 2022, 251: 111113
|
| [45] |
HinostrozaMA, XuH, Guedes SoaresCGuedes SoaresC, TeixeiraA P. Experimental and numerical simulations of zig-zag manoeuvres of a self-running ship model. Maritime Transportation and Harvesting of Sea Resources, 2017, London, UK, Taylor & Francis Group: 563-570
|
| [46] |
HochbaumA, SternF, AgdrupK, BrogliaR, KimS, PerdonP, QuadvliegF, YasukawaH, ZouZThe Manoeuvring Committee Final Report and Recommendations to the 25th ITTC, 2008
|
| [47] |
HwangWApplication of system identification to ship maneuvering, 1980, Cambridge, USA, Massachusetts Institute of Technology
|
| [48] |
InoueS, HiranoM, KijimaK, TakashinaJ. A practical calculation method of ship maneuvering motion. Int. Shipbuild. Prog., 1981, 28: 207-222
|
| [49] |
InoueS, MurayamaKCalculation of turning ship derivatives in shallow water, 1969
|
| [50] |
ITTC. Recommended procedures and guidelines-captive model test. The 28th International Towing Tank Conference, Wuxi, China, 20177.5-02-06-02
|
| [51] |
ITTC. Recommended procedures and guidelines - full scale manoeuvring trials. The 28th International Towing Tank Conference, Wuxi, China, 20177.5-04-02-01
|
| [52] |
ITTC. Final report and recommendations to the 24th ITTC. 24th International Towing Tank Conference, 2005369-408
|
| [53] |
ITTC. Recommended procedures and guidelines: Manoeuvrability captive model test procedure. The 23rd International Towing Tank Conference, ITTC’02, Venice, Italy, 2002
|
| [54] |
ITTC. Recommended procedures and guidelines: Free running model tests. The 23rd International Towing Tank Conference, ITTC’02, Venice, Italy, 2002
|
| [55] |
JeonM, YoonHK, ParkJ, RheeSH, SeoJ. Identification of 4-DoF maneuvering mathematical models for a combatant in intact and damaged conditions. Int. J. Nav. Archit. Ocean Eng., 2022, 14: 100480
|
| [56] |
JiangY, WangXG, ZouZJ, YangZL. Identification of coupled response models for ship steering and roll motion using support vector machines. Appl. Ocean Res., 2021, 110: 102607
|
| [57] |
JiangY, HouXR, WangXG, WangZH, YangZL, ZouZJ. Identification modeling and prediction of ship maneuvering motion based on LSTM deep neural network. Journal of Marine Science and Technology (Japan), 2022, 27(1): 125-137
|
| [58] |
KijimaK, NakiriY. On the practical prediction method for ship manoeuvring characteristics. International Conference on Marine Simulation and Ship Maneuverability MARSIM’03. Kanazawa, Japan, 2003RC-6-1-RC-2-10
|
| [59] |
KimD, BenedictK, PaschenM. Estimation of hydrodynamic coefficients from sea trials using a system identification method. J. Korean Soc. Mar. Environ. Saf., 2017, 23: 258-265
|
| [60] |
LataireE, VantorreM, DelefortrieG, CandriesM. Mathematical modelling of forces acting on ships during lightering operations. Ocean Eng., 2012, 55: 101-115
|
| [61] |
LiS, WangT, LiG, ZhangH. Ship maneuvering model optimization for improved identification with less excitation. Ocean Engineering, 2023, 280: 114540
|
| [62] |
LiJ, ZhangG, ZhangX, ZhangW. Integrating dynamic event-triggered and sensor-tolerant control: Application to USV-UAVs cooperative formation system for maritime parallel search. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(5): 3986-3998
|
| [63] |
LiuSY, OuyangZL, ChenG, ZhouX, ZouZJ. Black-box modeling of ship maneuvering motion based on Gaussian process regression with wavelet threshold denoising. Ocean Engineering, 2023, 271: 113765
|
| [64] |
LiuB, JinY, MageeAR, YiewLJ, ZhangS. System identification of Abkowitz model for ship maneuvering motion based on ε-support vector regression. Proc. Int. Conf. Offshore Mech. Arct. Eng., 2019
|
| [65] |
LjungLSystem identification: theory for the user, 1999
|
| [66] |
LuoW. Parameter identifiability of ship manoeuvring modeling using system identification. Math. Probl. Eng., 2016, 2016: 1-10
|
| [67] |
LuoW, Guedes SoaresC, ZouZ. Parameter identification of ship maneuvering model based on support vector machines and particle swarm optimization. J. Offshore Mech. Arct. Eng., 2016, 138: 031101
|
| [68] |
LuoW, LiX. Measures to diminish the parameter drift in the modeling of ship manoeuvring using system identification. Appl. Ocean Res., 2017, 67: 9-20
|
| [69] |
LuoW, MoreiraL, Guedes SoaresC. Manoeuvring simulation of catamaran by using implicit models based on support vector machines. Ocean Eng., 2014, 82: 150-159
|
| [70] |
LuoW, ZhangZ. Modeling of ship maneuvering motion using neural networks. J. Mar. Sci. Appl., 2016, 15: 426-432
|
| [71] |
LuoWL, ZouZJ. Parametric identification of ship maneuvering models by using support vector machines. J. Sh. Res., 2009, 53: 19-30
|
| [72] |
MatsunagaM. Method of predicting ship manoeuvrability in deep and shallow waters as a function of loading condition. NK Tech. Bull., 1993, 11: 51-59
|
| [73] |
MengY, ZhangX, ZhuJ. Parameter identification of ship motion mathematical model based on full-scale trial data. Int. J. Nav. Archit. Ocean Eng., 2022, 14: 100437
|
| [74] |
MiyauchiY, MakiA, UmedaN, RachmanDM, AkimotoY. System parameter exploration of ship maneuvering model for automatic docking/berthing using CMA-ES. J. Mar. Sci. Technol., 2022, 27: 1065-1083
|
| [75] |
MoreiraL, Guedes SoaresC. Simulating ship manoeuvrability with artificial neural networks trained by a short noisy data set. J. Mar. Sci. Eng., 2022, 11: 15
|
| [76] |
MoreiraL, Guedes SoaresC. Recursive neural network model of catamaran manoeuvring. Int. J. Marit. Eng., 2012, 154: 121-130
|
| [77] |
MoreiraL, Guedes SoaresC. Dynamic model of manoeuvrability using recursive neural networks. Ocean Eng., 2003, 30: 1669-1697
|
| [78] |
MoreiraL, Guedes SoaresC. Comparison between manoeuvring trials and simulations with recursive neural networks. Sh. Technol. Res., 2003, 50: 77-84
|
| [79] |
NomotoK, TaguchiK, HondaK, HiranoS. On the steering qualities of ships. J. Zosen Kiokai, 1956, 1956: 75-82
|
| [80] |
OkudaR, YasukawaH, MatsudaA. Validation of maneuvering simulations for a KCS at different forward speeds using the 4-DOF MMG method. Ocean Engineering, 2023, 284: 115174
|
| [81] |
OkudaR, YasukawaH, SanoM, HirataN, YoshimuraY, FurukawaY, MatsudaA. Maneuvering simulations of twin-propeller and twin-rudder ship in shallow water using equivalent single rudder model. Journal of Marine Science and Technology (Japan), 2022, 27(2): 948-970
|
| [82] |
OuyangZL, ChenG, ZouZJ. Identification modeling of ship maneuvering motion based on local Gaussian process regression. Ocean Engineering, 2023, 267: 113251
|
| [83] |
OuyangZL, ZouZJ. Nonparametric modeling of ship maneuvering motion based on Gaussian process regression optimized by genetic algorithm. Ocean Eng., 2021, 238: 109699
|
| [84] |
OuyangZL, ZouZJ, ZouL. Adaptive hybrid-kernel function based Gaussian process regression for nonparametric modeling of ship maneuvering motion. Ocean Engineering, 2023, 268: 113373
|
| [85] |
OgawaA, KasaiH. On the mathematical model of manoeuvring motion of ships. Int. Shipbuild. Prog., 1978, 25: 306-319
|
| [86] |
PapanikolaouA, ZaraphonitisG, Bitner-GregersenE, ShigunovV, MoctarO E, Guedes SoaresC, ReddyDN, SprengerF. Energy Efficient Safe Ship Operation (SHOPERA). Transp. Res. Procedia, 2016, 14: 820-829
|
| [87] |
PereraLP, MoreiraL, SantosFP, FerrariV, SutuloS, Guedes SoaresC. A navigation and control platform for real-time manoeuvring of autonomous ship models. IFAC Proceedings Volumes (IFAC-PapersOnline), 2012465-470
|
| [88] |
PereraLP, OliveiraP, Guedes SoaresC. System identification of vessel steering with unstructured uncertainties by persistent excitation maneuvers. IEEE J. Ocean. Eng., 2016, 41: 515-528
|
| [89] |
PereraLP, OliveiraP, Guedes SoaresC. System identification of nonlinear vessel steering. J. Offshore Mech. Arct. Eng., 2015, 137: 031302
|
| [90] |
PerezT, FossenTI. Practical aspects of frequency-domain identification of dynamic models of marine structures from hydrodynamic data. Ocean Eng., 2011, 38: 426-435
|
| [91] |
Pires da SilvaP, HinostrozaMA, SutuloS, Guedes SoaresCGuedes SoaresC, SantosT A. Instrumentation and data acquisition system for full-scale manoeuvrability tests on board of naval surface ships. Developments in Maritime Technology and Engineering, 2021227-234
|
| [92] |
Pires da SilvaP, SutuloS, Guedes SoaresC. Sensitivity analysis of ship manoeuvring mathematical models. J. Mar. Sci. Eng., 2023, 11: 416
|
| [93] |
RajeshG, BhattacharyyaSK. System identification for nonlinear maneuvering of large tankers using artificial neural network. Appl. Ocean Res., 2008, 30: 256-263
|
| [94] |
Revestido HerreroEE, Velasco GonzalezFJ. Two-step identification of non-linear manoeuvring models of marine vessels. Ocean Eng., 2012, 53: 72-82
|
| [95] |
RidaoP, CarrerasM, RibasD, SanzPJ, OliverG. Intervention AUVs: The next challenge. Annu. Rev. Control, 2015, 40: 227-241
|
| [96] |
RossANonlinear manoeuvring models for ships: A Lagrangian approach, 2008
|
| [97] |
RossA, SelvikO, HassaniV, RingenE, FathiD. Identification of nonlinear manoeuvring models for marine vessels using planar motion mechanism tests. 34th International Conference on Ocean, Offshore and Arctic Engineering, 2015, Newfoundland, Canada, ASME: V007T06A014
|
| [98] |
Selvik, BergTE, GavrilinS. Sea trials for validation of shiphandling simulation models-a case study. Maritime-Port Technology and Development-Proceedings of the International Conference on Maritime and Port Technology and Development, MTEC 2014, 2015, Balkema, CRC Press: 141-146
|
| [99] |
SilvaKM, MakiKJ. Data-driven system identification of 6-DoF ship motion in waves with neural networks. Appl. Ocean Res., 2022, 125: 103222
|
| [100] |
SongL, HaoL, TaoH, XuC, GuoR, LiY, YaoJ. Research on black-box modeling prediction of usv maneuvering based on SSA-WLS-SVM. Journal of Marine Science and Engineering, 2023, 11(2): 324
|
| [101] |
SørensenAJ. A survey of dynamic positioning control systems. Annu. Rev. Control, 2011, 35: 123-136
|
| [102] |
SutuloS, Guedes SoaresC. An algorithm for optimized design of maneuvering experiments. Journal of Ship Research, 2002, 46(3): 214-227
|
| [103] |
SutuloS, Guedes SoaresC. Synthesis of experimental designs of maneuvering captive-model tests with a large number of factors. Journal of Marine Science and Technology, 2004, 9(1): 32-42
|
| [104] |
SutuloS, Guedes SoaresCGuedes SoaresC, GarbatovY, FonsecaN, TeixeiraA P. Mathematical models for simulation of manoeuvring performance of ships. Maritime Engineering and Technology, 2011, London, Taylor & Francis Group: 661-698
|
| [105] |
SutuloS, Guedes SoaresC. An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests. Ocean Eng., 2014, 79: 10-25
|
| [106] |
SutuloS, Guedes SoaresC. On the application of empiric methods for prediction of ship manoeuvring properties and associated uncertainties. Ocean Eng., 2019, 186: 106111
|
| [107] |
SuykensJAK, Van GestelT, De BrabanterJ, De MoorB, VandewalleJLeast squares support vector machines, 2002
|
| [108] |
TatinatiS, WangY, ShafiqG, VeluvoluKC. Online LS-SVM based multi-step prediction of physiological tremor for surgical robotics. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013, 20136043-6046
|
| [109] |
van de VenPWJ, JohansenTA, SørensenAJ, FlanaganC, ToalD. Neural network augmented identification of underwater vehicle models. Control Eng. Pract., 2007, 15: 715-725
|
| [110] |
VapnikVNStatistical learning theory, 1998
|
| [111] |
VapnikVNThe nature of statistical learning theory, 19958 187
|
| [112] |
VarelaJM, Guedes SoaresC. Software architecture of an interface for three-dimensional collision handling in maritime virtual environments. Simulation, 2015, 91: 735-749
|
| [113] |
VarelaJM, Guedes SoaresC. Interactive 3D desktop ship simulator for testing and training offloading manoeuvres. Appl. Ocean Res., 2015, 51: 367-380
|
| [114] |
WakitaK, MakiA, UmedaN, MiyauchiY, ShimojiT, RachmanDM, AkimotoY. On neural network identification for low-speed ship maneuvering model. J. Mar. Sci. Technol., 2022, 27: 772-785
|
| [115] |
WangN, ErMJ, HanM. Large tanker motion model identification using generalized ellipsoidal basis function-based fuzzy neural networks. IEEE Trans. Cybern., 2015, 45: 2732-2743
|
| [116] |
WangS, WangL, ImN, ZhangW, LiX. Real-time parameter identification of ship maneuvering response model based on nonlinear Gaussian Filter. Ocean Eng., 2022, 247: 110471
|
| [117] |
WangT, LiG, WuB, ÆsøyV, ZhangH. Parameter identification of ship manoeuvring model under disturbance using support vector machine method. Ships and Offshore Structures, 2021, 16: 13-21
|
| [118] |
WangX, ZhaoJ, LiuS, GengT. A constraint multi-step prediction method for identification of a water-jet vessel in 3DOF planar motion. Ocean Eng., 2021, 237: 109534
|
| [119] |
WangZ, XuH, XiaL, ZouZ, Guedes SoaresC. Kernel-based support vector regression for nonparametric modeling of ship maneuvering motion. Ocean Eng., 2020, 216: 107994
|
| [120] |
WangZ, ZouZ, Guedes SoaresC. Identification of ship manoeuvring motion based on nu-support vector machine. Ocean Eng., 2019, 183: 270-281
|
| [121] |
WangZ, ZouZ, Guedes SoaresC. Identification of ship manoeuvring motion based on nu-support vector machine. Ocean Eng., 2019, 183: 270-281
|
| [122] |
WooJ, ParkJ, YuC, KimN. Dynamic model identification of unmanned surface vehicles using deep learning network. Appl. Ocean Res., 2018, 78: 123-133
|
| [123] |
WuG, ZhangJ, LiG, WangL, YuQ, GuoJ. Identification method of nonlinear maneuver model for unmanned surface vehicle from sea trial data based on support vector machine. J. Mech. Sci. Technol., 2022, 36: 4257-4267
|
| [124] |
XuH, Guedes SoaresC. Convergence analysis of hydrodynamic coefficients estimation using regularization filter functions on free-running ship model tests with noise. Ocean Eng., 2022, 250: 111012
|
| [125] |
XuH, Guedes SoaresC. Manoeuvring modelling of a containership in shallow water based on optimal truncated nonlinear kernel-based least square support vector machine and quantum-inspired evolutionary algorithm. Ocean Eng., 2020, 195: 106676
|
| [126] |
XuH, Guedes SoaresC. Hydrodynamic coefficient estimation for ship manoeuvring in shallow water using an optimal truncated LS-SVM. Ocean Eng., 2019, 191: 106488
|
| [127] |
XuH, HassaniV, Guedes SoaresC. Truncated least square support vector machine for parameter estimation of a nonlinear manoeuvring model based on PMM tests. Appl. Ocean Res., 2020, 97: 102076
|
| [128] |
XuH, HassaniV, Guedes SoaresC. Comparing generic and vectorial nonlinear manoeuvring models and parameter estimation using optimal truncated least square support vector machine. Appl. Ocean Res., 2020, 97: 102061
|
| [129] |
XuH, HassaniV, Guedes SoaresC. Uncertainty analysis of the hydrodynamic coefficients estimation of a nonlinear manoeuvring model based on planar motion mechanism tests. Ocean Eng., 2019, 173: 450-459
|
| [130] |
XuH, HassaniV, Guedes SoaresC. Parameters estimation of nonlinear manoeuvring model for marine surface ship based on PMM tests. 37th International Conference on Ocean, Offshore and Arctic Engineering, Madrid, Spain, 2018V11BT12A010
|
| [131] |
XuH, HassaniV, HinostrozaMA, Guedes SoaresC. Real-time parameter estimation of nonlinear vessel steering model using support vector machine. 37th International Conference on Ocean, Offshore and Arctic Engineering, Madrid, Spain, 2018V11BT12A009
|
| [132] |
XuH, HinostrozaMA, Guedes SoaresC. Estimation of hydrodynamic coefficients of a nonlinear manoeuvring mathematical model with free-running ship model tests. Int. J. Marit. Eng., 2018, 160: A-213-A-226
|
| [133] |
XuH, HinostrozaMA, HassaniV, Guedes SoaresC. Real-time parameter estimation of a nonlinear vessel steering model using a support vector machine. J. Offshore Mech. Arct. Eng., 2019, 141: 061606
|
| [134] |
XuH, HinostrozaMA, WangZ, Guedes SoaresC. Experimental investigation of shallow water effect on vessel steering model using system identification method. Ocean Eng., 2020, 199: 106940
|
| [135] |
XuH, da SilvaPP, Guedes SoaresC. Effect of sampling rate in sea trial tests on the estimation of hydrodynamic parameters for a nonlinear ship manoeuvring model. Journal of Marine Science and Engineering, 2024, 12(3): 407
|
| [136] |
XuP, ChengC, ChengHX, ShenYL, DingYX. Identification-based 3 DOF model of unmanned surface vehicle using support vector machines enhanced by cuckoo search algorithm. Ocean Engineering, 2020, 197: 106898
|
| [137] |
XueY, ChenG, LiZ, XueG, WangW, LiuY. Online identification of a ship maneuvering model using a fast noisy input Gaussian process. Ocean Eng., 2022, 250: 110704
|
| [138] |
XueY, LiuY, JiC, XueG. Hydrodynamic parameter identification for ship manoeuvring mathematical models using a Bayesian approach. Ocean Eng., 2020, 195: 106612
|
| [139] |
XueY, LiuY, JiC, XueG, HuangS. System identification of ship dynamic model based on Gaussian process regression with input noise. Ocean Eng., 2020, 216: 107862
|
| [140] |
XueY, LiuY, XueG, ChenG. Identification and prediction of ship maneuvering motion based on a Gaussian process with uncertainty propagation. J. Mar. Sci. Eng., 2021, 9: 804
|
| [141] |
YangB, ZhangG, RaoH, WangS, YangB, SunZ. Numerical simulation of the maneuvering performance of ships in broken ice area. Ocean Engineering, 2024, 294: 116783
|
| [142] |
YangY, ChillcceG, el MoctarO. Mathematical modeling of shallow water effects on ship maneuvering. Applied Ocean Research, 2023, 136: 103573
|
| [143] |
YangY, el MoctarO. A mathematical model for ships maneuvering in deep and shallow waters. Ocean Engineering, 2024, 295: 116927
|
| [144] |
YasukawaH, YoshimuraY. Introduction of MMG standard method for ship maneuvering predictions. J. Mar. Sci. Technol., 2015, 20: 37-52
|
| [145] |
YinJ, RenH, ZhouY. The whole ship simulation training platform based on virtual reality. IEEE Open J. Intell. Transp. Syst., 2021, 2: 207-215
|
| [146] |
YoonHK, RheeKP. Identification of hydrodynamic coefficients in ship maneuvering equations of motion by Estimation-Before-Modeling technique. Ocean Eng., 2003, 30: 2379-2404
|
| [147] |
YueJ, LiuL, GuN, PengZ, WangD, DongY. Online adaptive parameter identification of an unmanned surface vehicle without persistency of excitation. Ocean Eng., 2022, 250: 110232
|
| [148] |
ZhangG, ZhangX, PangH. Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scale trial data. ISA Trans., 2015, 58: 186-195
|
| [149] |
ZhangX, MengY, LiuZ, ZhuJ. Modified grey wolf optimizer-based support vector regression for ship maneuvering identification with full-scale trial. J. Mar. Sci. Technol., 2022, 27: 576-588
|
| [150] |
ZhangXG, ZouZJ. Identification of Abkowitz model for ship manoeuvring motion using ε-support vector regression. J. Hydrodyn., 2011, 23: 353-360
|
| [151] |
ZhangYY, WangZH, ZouZJ. Black-box modeling of ship maneuvering motion based on multi-output nu-support vector regression with random excitation signal. Ocean Eng., 2022, 257: 111279
|
| [152] |
ZhangZ, RenJ, BaiW. MIMO non-parametric modeling of ship maneuvering motion for marine simulator using adaptive moment estimation locally weighted learning. Ocean Engineering, 2022, 261: 112103
|
| [153] |
ZhangZ, ZhangY, WangJ, WangH. Parameter identification and application of ship maneuvering model based on TO-CSA. Ocean Engineering, 2022, 266: 113128
|
| [154] |
ZhaoB, ZhangX, LiangC. A novel parameter identification algorithm for 3-DOF ship maneuvering modelling using nonlinear multi-innovation. J. Mar. Sci. Eng., 2022, 10: 581
|
| [155] |
ZhaoY, WuJ, ZengC, HuangY. Identification of hydrodynamic coefficients of a ship manoeuvring model based on PRBS input. Ocean Eng., 2022, 246: 110640
|
| [156] |
ZhuM, HahnA, WenY-Q, BollesA. Identification-based simplified model of large container ships using support vector machines and artificial bee colony algorithm. Appl. Ocean Res., 2017, 68: 249-261
|
| [157] |
ZhuM, HahnA, WenY, BollesA. Parameter identification of ship maneuvering models using recursive least square method based on support vector machines. TransNav, Int. J. Mar. Navig. Saf. Sea Transp., 2017, 11: 23-29
|
| [158] |
ZhuM, WangT, ZhangH, LiG. Ship manoeuvring model identification under wind disturbance. 2022 IEEE Int. Conf. Real-time Comput. Robot., 2022648-653
|
RIGHTS & PERMISSIONS
The Author(s)