Research Papers: Ocean Engineering

Parameter Identification of Ship Maneuvering Model Based on Support Vector Machines and Particle Swarm Optimization

[+] Author and Article Information
Weilin Luo

Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Lisbon 1049-001, Portugal;
College of Mechanical
Engineering and Automation,
Fuzhou University,
Fujian 350108, China

C. Guedes Soares

Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Lisbon 1049-001, Portugal
e-mail: c.guedes.soares@centec.tecnico.ulisboa.pt

Zaojian Zou

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 March 30, 2013; final manuscript received January 21, 2016; published online April 6, 2016. Editor: Solomon Yim.

J. Offshore Mech. Arct. Eng 138(3), 031101 (Apr 06, 2016) (8 pages) Paper No: OMAE-13-1028; doi: 10.1115/1.4032892 History: Received March 30, 2013; Revised January 21, 2016

Combined with the free-running model tests of KVLCC ship, the system identification (SI) based on support vector machines (SVM) is proposed for the prediction of ship maneuvering motion. The hydrodynamic derivatives in an Abkowitz model are determined by the Lagrangian factors and the support vectors in the SVM regression model. To obtain the optimized structural factors in SVM, particle swarm optimization (PSO) is incorporated into SVM. To diminish the drift of hydrodynamic derivatives after regression, a difference method is adopted to reconstruct the training samples before identification. The validity of the difference method is verified by correlation analysis. Based on the Abkowitz mathematical model, the simulation of ship maneuvering motion is conducted. Comparison between the predicted results and the test results demonstrates the validity of the proposed methods in this paper.

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International Maritime Organization (IMO), 2002, “ Standards for Ship Maneuverability,” Resolution MSC 137(76).
The Maneuvering Committee, 2008, “ Final Report and Recommendations to the 25th ITTC,” 25th International Towing Tank Conference, Fukuoka, Japan, pp. 143–208.
Koyama, K. , 1971, “ Analysis of Full-Scale Measurement of Maneuverability by Trial and Error Methods,” Shipbuilding Laboratory, Delft University of Technology, Delft, The Netherlands, Report No. 332.
Holzhüter, T. , 1989, “ Robust Identification in an Adaptive Track Controller for Ships,” 3rd IFAC Symposium on Adaptive Systems in Control and Signal Processing, Glasgow, UK, pp. 461–466.
Hayes, M. N. , 1971, “ Parameters Identification of Nonlinear Stochastic Systems Applied to Ocean Vehicle Dynamics,” Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA.
Van Amerongen, J. , 1984, “ Adaptive Steering of Ships—A Model Reference Approach,” Automatica, 20(1), pp. 3–14. [CrossRef]
Abkowitz, M. A. , 1980, “ Measurement of Hydrodynamic Characteristic From Ship Maneuvering Trials by System Identification,” Trans. Soc. Naval Arch. Mar. Eng., 88, pp. 283–318.
Hwang, W.-Y. , 1980, “ Application of System Identification to Ship Maneuvering,” Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA.
Åström, K. J. , and Källström, C. G. , 1976, “ Identification of Ship Steering Dynamics,” Automatica, 12(1), pp. 9–22. [CrossRef]
Källström, C. G. , and Åström, K. J. , 1981, “ Experiences of System Identification Applied to Ship Steering,” Automatica, 17(1), pp. 187–198. [CrossRef]
Zhou, W.-W. , and Blanke, M. , 1989, “ Identification of a Class of Nonlinear State-Space Models Using RPE Techniques,” IEEE Trans. Autom. Control, 34(3), pp. 312–316. [CrossRef]
Munoz-Mansilla, R. , Aranda, J. , Diaz, J. M. , and de la Cruz, J. , 2009, “ Parameter Model Identification of High-Speed Craft Dynamics,” Ocean Eng., 36, pp. 1025–1038. [CrossRef]
Nguyen, H. D. , 2007, “ Recursive Identification of Ship Maneuvering Dynamics and Hydrodynamics,” 36th EMAC Annual Conference, Reykjavik, Iceland, pp. C717–C732.
Ødegård, V. , 2009, “ Nonlinear Identification of Ship Autopilot Models,” Master thesis, Norwegian University of Science and Technology, Trondheim, Norway.
Velasco, F. J. , Zamanillo, I. , Lopez, E. , and Moyano, E. , 2011, “ Parameter Estimation of Ship Linear Maneuvering Models,” OCEANS 2011 IEEE Santander Conference, Santander (Cantabria), Spain, pp. 1–8.
Herrero, E. R. , and Velasco Gonzalez, F. , 2012, “ Two-Step Identification of Non-Linear Maneuvering Models of Marine Vessels,” Ocean Eng., 53, pp. 72–82. [CrossRef]
Perera, L. P. , Oliveira, P. , and Guedes Soares, C. , 2015, “ System Identification of Nonlinear Vessel Steering,” ASME J. Offshore Mech. Arct. Eng, 137(3), p. 031302.
Yoon, H. K. , and Rhee, K. P. , 2003, “ Identification of Hydrodynamic Derivatives in Ship Maneuvering Equations of Motion by Estimation-Before-Modeling Technique,” Ocean Eng., 30(18), pp. 2379–2404. [CrossRef]
Araki, M. , Sadat-Hosseini, H. , Sanada, Y. , Tanimoto, K. , Umeda, N. , and Stern, F. , 2012, “ Estimating Maneuvering Coefficients Using System Identification Methods With Experimental, System-Based, and CFD Free-Running Trial Data,” Ocean Eng., 51, pp. 63–84. [CrossRef]
Selvam, R. P. , Bhattacharyya, S. K. , and Haddara, M. R. , 2005, “ A Frequency Domain System Identification Method for Linear Ship Maneuvering,” Int. Shipbuild. Prog., 52(1), pp. 5–27.
Bhattacharyya, S. K. , and Haddara, M. R. , 2006, “ Parameter Identification for Nonlinear Ship Maneuvering,” J. Ship Res., 50(3), pp. 197–207.
Chen, Y. , Song, Y. , and Chen, M. , 2010, “ Parameters Identification for Ship Motion Model Based on Particle Swarm Optimization,” Kybernetes, 39(6), pp. 871–880. [CrossRef]
Haddara, M. R. , and Wang, Y. , 1999, “ Parametric Identification of Maneuvering Models for Ships,” Int. Shipbuild. Prog., 46(445), pp. 5–27.
Ebada, A. , and Abdel-Maksoud, M. , 2005, “ Applying Artificial Intelligence (A.I.) to Predict the Limits of Ship Turning Maneuvers,” Jahrb. Schiffbautechnischen Ges., 99, pp. 132–139.
Moreira, L. , and Guedes Soares, C. , 2003, “ Dynamic Model of Maneuverability Using Recursive Neural Networks,” Ocean Eng., 30(13), pp. 1669–1697. [CrossRef]
Hess, D. , and Faller, W. , 2000, “ Simulation of Ship Maneuvers Using Recursive Neural Networks,” 23rd Symposium on Naval Hydrodynamics, Val de Reuil, France, pp. 223–242.
Hess, D. , and Faller, W. , 2002, “ Using Recursive Neural Networks for Blind Predictions of Submarine Maneuvers,” 24th Symposium on Naval Hydrodynamics, Fukuoka, Japan, pp. 719–733.
Hess, D. , Faller, W. , Lee, J. , Fu, T. , and Ammeen, E. , 2006, “ Ship Maneuvering Simulation in Wind and Waves: A Nonlinear Time-Domain Approach Using Recursive Neural Networks,” 26th Symposium on Naval Hydrodynamics, Rome, Italy.
Hess, D. , Faller, W. , and Lee, J. , 2008, “ Real-Time Nonlinear Simulation of Maneuvers for U.S. Navy Combatant DTMB 5415,” Workshop on Verification and Validation of Ship Maneuvering Simulation Methods (SIMMAN 2008), Copenhagen, Denmark, pp. E15–E21.
Rajesh, G. , and Bhattacharyya, S. K. , 2008, “ System Identification for Nonlinear Maneuvering of Large Tankers Using Artificial Neural Network,” Appl. Ocean Res., 30(4), pp. 256–263. [CrossRef]
Luo, W. L. , and Zou, Z. J. , 2009, “ Parametric Identification of Ship Maneuvering Models by Using Support Vector Machines,” J. Ship Res., 53(1), pp. 19–30.
Luo, W. L. , and Zou, Z. J. , 2010, “ Blind Prediction of Ship Maneuvering by Using Support Vector Machines,” ASME Paper No. OMAE2010-20723.
Luo, W. L. , Zou, Z. J. , and Xiang, H. L. , 2011, “ Simulation of Ship Maneuvering in the Proximity of a Pier by Using Support Vector Machines,” ASME Paper No. OMAE2010-20723.
Zhang, X. G. , and Zou, Z. J. , 2011, “ Identification of Abkowitz Model for Ship Maneuvering Motion Using ε–Support Vector Machines,” J. Hydrol., 23(3), pp. 353–360.
Xu, F. , Zou, Z. J. , Yin, J. C. , and Cao, J. , 2013, “ Identification Modeling of Underwater Vehicles' Nonlinear Dynamics Based on Support Vector Machines,” Ocean Eng., 67, pp. 68–76. [CrossRef]
Zhang, X. G. , and Zou, Z. J. , 2013, “ Estimation of the Hydrodynamic Coefficients From Captive Model Test Results by Using Support Vector Machines,” Ocean Eng., 73, pp. 25–31. [CrossRef]
Wang, X. G. , Zou, Z. J. , and Xu, F. , 2013, “ Modeling of Ship Maneuvering Motion in 4 degrees of Freedom Based on Support Vector Machines,” ASME Paper No. OMAE2013-10806.
Moreno-Salinas, D. , Chaos, D. , Manuel de la Cruz, J. , and Aranda, J. , 2013, “ Identification of a Surface Marine Vessel Using LS-SVM,” J. Appl. Math., 2013, pp. 1–11. [CrossRef]
Abkowitz, M. A. , 1964, “ Lectures on Ship Hydrodynamics—Steering and Maneuverability,” Hydro- and Aerodynamics Laboratory, Lyngby, Denmark, Report No. Hy-5.
Fossen, T. I. , 1994, Guidance and Control of Ocean Vehicles, Appx. E., Wiley, New York.
Vapnik, V. N. , 1998, Statistical Learning Theory, Wiley, New York, Chap. 1.
Suykens, J. A. K. , De Brabanter, J. , Lukas, L. , and Vandewalle, J. , 2002, “ Weighted Least Squares Support Vector Machines: Robustness and Sparse Approximation,” Neurocomputing, 48, pp. 85–105. [CrossRef]
Hwang, W.-Y. , 1982, “ Cancellation Effect and Parameter Identifiability of Ship Steering Dynamics,” Int. Shipbuild. Prog., 26, pp. 90–120.
Gustafsson, F. , 1996, “ Determining the Initial States in Forward-Backward Filtering,” IEEE Trans. Signal Process., 44(4), pp. 988–992. [CrossRef]
Yeon, S. M. , Yeo, D. J. , and Rhee, K. P. , 2006, “ Optimal Input Design for the Identification of Low-Speed Maneuvering Mathematical Model,” International Conference on Marine Simulation and Ship Maneuverability (MARSIM2006), Terschelling, The Netherlands.
Vapnik, V. N. , Levin, E. , and Lecun, Y. , 1994, “ Measuring the VC-Dimension of a Learning Machine,” Neural Comput., 6(5), pp. 851–876. [CrossRef]
Kennedy, J. , and Eberhart, R. , 1995, “ Particle Swarm Optimization,” IEEE International Conference on Neural Networks, Washington, DC, Vol. 4, pp. 1942–1948.
Stern, F. , Agdrup, K. , Kim, S. Y. , Hochbaum, A. C. , Rhee, K. P. , Quadvlieg, F. , Perdon, P. , Hino, T. , Broglia, R. , and Gorski, J. , 2011, “ Experience From SIMMAN 2008—The First Workshop on Verification and Validation of Ship Maneuvering Simulation Methods,” J. Ship Res., 55(2), pp. 135–147.


Grahic Jump Location
Fig. 1

Coordinate system of ship maneuvering motion

Grahic Jump Location
Fig. 2

Framework of SVM regression

Grahic Jump Location
Fig. 3

Hull, propeller, and rudder profiles of KVLCC ships

Grahic Jump Location
Fig. 5

Prediction of 25 deg/5 deg zigzag maneuver

Grahic Jump Location
Fig. 6

Prediction of 35 deg/5 deg zigzag maneuver



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