Abstract
By anac10°/10° zigzag test, the hydrodynamic derivatives in the Abkowitz model for ship manoeuvring motion are identified by using ε -Support Vector Regression (ε -SVR). To damp the extent of parameter drift, a series of random numbers are added into the training samples to reconstruct the training samples. The identification results of the hydrodynamic derivatives are compared with the Planar Motion Mechanism (PMM) test results to verify the identification method. By using the identified Abkowitz model, 20°/20° zigzag test is numerically simulated. The simulated results are compared with those obtained by using the Abkowitz model where the hydrodynamic derivatives are obtained from PMM tests. The agreement is satisfactory, which shows that the regressive Abkowitz model has a good generalization performance.
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References
ABKOWITZ M. A. Measurement of hydrodynamic characteristic from ship maneuvering trials by system identification[J]. Transactions of Society of Naval Architects and Marine Engineers, 1980, 88: 283–318.
OGAWA A., KASAI H. On the mathematical model of manoeuvring motion of ship[J]. International Shipbuilding Progress, 1978, 25(292): 306–319.
NOMOTO K., TAGUCHI T. and HONDA K. et al. On the steering qualities of ships[J]. International Shipbuilding Progress, 1957, 35(4): 354–370
MAHFOUZ A. B., HADDARA M. R. Effects of the damping and excitation on the identification of the hydrodynamic parameters for an underwater robotic vehicle[J]. Ocean Engineering, 2003, 30(8): 1005–1025.
SELVAM R. P., BHATTACHARYYA S. K. and HADDARA M. R. A frequency domain system identification method for linear ship manoeuvring[J]. International Shipbuilding Progress, 2005, 52(1): 5–27.
BHATTACHARYYA S. K., HADDARA M. R. Parameter identification for nonlinear ship manoeuvring[J]. Journal of Ship Research, 2006, 50(3): 197–207.
EBADA A., ABDEL-MAKSOUD M. Applying artificial intelligence (A.I.) to predict the limits of ship turning manoeuvres[J]. Jahrbuch der Schiffbautechnischen Gesellschaft, 2005, 99: 132–139.
MOREIRA L., GUEDES SOARES C. Dynamic model of manoeuvrability using recursive neural networks[J]. Ocean Engineering, 2003, 30(13): 1669–1697.
CHIU F. C., CHANG T. L. and GO J. et al. A recursive neural networks model for ship maneuverability prediction[C]. Proceedings of Ocean’04, MTS/IEEE Texhno-Ocean’04. Kobe, Japan, 2004, 3: 1211–1218.
VAPNIK V. N. Universal learning technology: Support vector machines[J]. NEC Journal of Advanced Technology, 2005, 2(2): 137–144.
LUO Wei-lin, ZOU Zao-jian and LI Tie-shan. Application of support vector machine to ship steering[J]. Journal of Shanghai Jiaotong University (Science), 2009, 14(2): 462–466.
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.
LUO W. L., ZOU Z. J. Prediction of ship mano- euvring by using support vector machines[C]. Work- shop on Verification and Validation of Ship Mano- euvring Simulation Methods, SIMMAN 2008. Copenhagen, Denmark, 2008, 1: E28–E32.
LUO W. L., ZOU Z. J. Parametric identification of ship manoeuvring models by using support vector machines[J]. Journal of Ship Research, 2009, 53(1): 19–30.
CHISLETT M. S., STRØM-TEJSEN J. Planar motion mechanism tests and full-scale steering and maneuvering predictions for a Mariner Class Vessel, Technical Report Hy-6, Hydro- and Aerodynamics Laboratory, Lyngby, Denmark, 1965.
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Project supported by the National Natural Science Foundation of China (Grant Nos. 50979060, 51079031), the Foundation of National Science and Technology Key Laboratory of Hydrodynamics (Grant No. 9140C2201091001).
Biography: ZHANG Xin-guang (1982-), Male, Ph. D. Candidate
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Zhang, Xg., Zou, Zj. Identification of Abkowitz Model for Ship Manoeuvring Motion Using ε -Support Vector Regression. J Hydrodyn 23, 353–360 (2011). https://doi.org/10.1016/S1001-6058(10)60123-0
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DOI: https://doi.org/10.1016/S1001-6058(10)60123-0
Key words
- ship manoeuvring
- Abkowitz model
- parameter identification
- ε -Support Vector Regression (ε -SVR)