Ship Maneuvering Prediction Using Grey Box Framework via Adaptive RM-SVM with Minor Rudder
By:
References
- 1. IMO (International Maritime Organization). Regulatory scoping exercise for the use of maritime autonomous surface ships (MASS), MSC 99-WP.9 Report of the working group, London: IMO, (2018).
- 2. Knud Benedict, Caspar Krueger, Gerd Milbradt, Michèle Schaub, simulation-augmented maneuvering system to support autonomous ships from the shore in different loading conditions, Autonomous Ship Technology Symposium 2016, Amsterdam, (2016).
- 3. Guo C. Y., Li X. Y., Wang S., Zhao D. G. A numerical simulation method for resistance prediction of ship in pack ice. Journal of Harbin Engineering University, (2016), 37(2):145-150.
- 4. ITTC (International Towing Tank Conference). The maneuvering committee, final report and recommendations to the 25th ITTC. Kgs. Lyngby, Denmark: ITTC, (2008).
- 5. Jia X, Yang Y. Ship motion mathematical model. Dalian, China: Dalian Maritime University Press, (1999).
- 6. Abkowitz M A. Measurement of hydrodynamic characteristics from ship maneuvering trials by system identification. Transactions of Society of Naval Architects and Marine Engineers, (1980), (88): 283-318.
- 7. Luo W L, Zou Z J. Identification of response models of ship maneuvering motion using support vector machines. Journal of Ship Mechanics, (2007), 11(6): 832-838.
- 8. Yin J C, Zou Z J, Xu F. Parametric Identification of Abkowitz Model for Ship Maneuvering Motion by Using Partial Least Squares Regression. Journal of Offshore Mechanics & Arctic Engineering, (2015), 137(3):031301-031308.
- 9. Zhang G. Q. Zhang, Zhang X K, Pang H S. Multi-innovation auto-constructed least squares identification for 4 DOF ship maneuvering modelling with full-scale trial data. ISA Transactions, (2015), 58:186.
- 10. C Källström, Åström, Karl Johan, Identification and Modelling of Ship Dynamics, volume 7015: Lund (Sweden): Identification and Modelling of Ship Dynamics, (1972)
- 11. Yoon H K, Rhee K P. Identification of hydrodynamic coefficients in ship maneuvering equations of motion by Estimation-Before-Modeling technique. Ocean Engineering, (2003), 30(18):2379-2404.
- 12. Sutulo S, Soares C. G. Offline system identification of ship maneuvering mathematical models with a global optimization algorithm. Marsim 2015: International Conference on Ship Maneuverability and Maritime Simulation. Tyne and Wear, United Kingdom: Newcastle University, (2015).
- 13. Haddara M R, Wang Y. Parametric identification of maneuvering models for ships. International Shipbuilding Progress, (1999), 46(445):5-27.
- 14. Wang N, Er M J, Han M. Large Tanker Motion Model Identification Using Generalized Ellipsoidal Basis Function-Based Fuzzy Neural Networks Design Automation Conference. Proceedings. ACM/IEEE. IEEE, (1988):205-210.
- 15. Faller H. D., W. Simulation of Ship Maneuvers Using Recursive Neural Networks [C]. Twenty-Third Symposium on Naval Hydrodynamics, National Academies Press, 2000.
- 16. Moreira L., Soares C G., Dynamic model of maneuverability using recursive neural networks. Ocean Engineering, (2003), 30(13):1669-1697.
- 17. Oskin D. D. Neural Network Identification of Marine Ship Dynamics Control Applications in Marine Systems. (2013):191-196.
- 18. Bai W. W., Ren J. S., Li T. S. Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for A Nonlinear Ship Maneuvering System. China Ocean Engineering, (2018), 32(3):288-300.
- 19. Zhu M, Hahn A, Wen Y, et al. Identification-based Simplified Model of Large Container Ships Using Support Vector Machines and Artificial Bee Colony Algorithm. Applied Ocean Research, (2017), 68:249-261.
- 20. Luo W, Li X. Measures to diminish the parameter drift in the modeling of ship maneuvering using system identification. Applied Ocean Research, (2017), 67:9-20.
- 21. Kijima K, Toshiyuki K, Yasuaki N, et al. On the maneuvering performance of ship with the parameter of loading condition. Jour of The Soc of Naval Architects of Japan, (1990), 168(3): 141-148.
- 22. Newman J. Marine Hydrodynamics. MIT, 1977.
- 23. Fossen T I. Handbook of Marine Craft Hydrodynamics and Motion Control. (2011).
- 24. Ljung L. Perspectives on system identification. Annual Reviews in Control, (2008), 34(1):1-12.
- 25. Ioannou P A, Sun J. Robust Adaptive Control. Springer London, (2015).
- 26. Wang X G, Zou Z J, Hou X R, et al. System identification modelling of ship maneuvering motion based on support vector regression. Journal of Hydrodynamics, 2015, 27(4):502-512.
- 27. IMO, Revision of the interim standards for ship maneuverability ship maneuvering data submitted by the Republic of Korea, (2000), sub-committee on ship design and equipment, 44th session, Agenda item 4.
- 28. SNAME. Nomenclature for treating the motion of a submerged body through a fluid. Technical and Research Bulletin. New York: The Society of Naval Architects and Marine Engineers, (1950).
- 29. Norrbin Nils H., Theory and Observations on the Use of a Mathematical Model for Ship Maneuvering in Deep and Confined Waters, Publication No.68 of SSPA. Sweden, (1970)
- 30. Ho C H, Lin C J. Large-scale linear support vector regression. Journal of Machine Learning Research, (2012), 13(1):3323-3348.
- 31. Wang X, Zou Z, Ren R, et al. Black-box modeling of ship maneuvering motion in 4degrees of freedom based on support vector machines. Shipbuilding of China, (2014), 55(3):147-155.
- 32. Sung Y J, Park S H. Prediction of Ship Maneuvering Performance Based on Virtual Captive Model Tests. Journal of the Society of Naval Architects of Korea, (2015), 52(5):407-417.
- 33. Yasukawa H, Yoshimura Y. Introduction of MMG standard method for ship maneuvering predictions. Journal of Marine Science & Technology, (2015), 20(1):37-52.
- 34. Liu H, Ma N, Gu X. Maneuvering Prediction of a VLCC Model Based on CFD Simulation for PMM Tests by Using a Circulating Water Channel ASME 2015, International Conference on Ocean, Offshore and Arctic Engineering. (2015):41548-41545.
Language: English
Page range: 115 - 127
Published on: Oct 18, 2019
Published by: Gdansk University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
Related subjects:
© 2019 Bin Mei, Licheng Sun, Guoyou Shi, Xiaodong Liu, published by Gdansk University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.