Ship maneuvering prediction using grey box framework via adaptive RM-SVM with minor rudder

• Autorzy: Me Bin , Sun Licheng , Shi Guoyou , Liu Xiaodong

Identyfikatory

DOI

10.2478/pomr-2019-0052

• Języki publikacji: EN

Abstrakty

EN

A grey box framework is applied to model ship maneuvering by using a reference model (RM) and a support vector machine (SVM) (RM-SVM). First, the nonlinear characteristics of the target ship are determined using the RM and the similarity rule. Then, the linear SVM adaptively fits the errors between acceleration variables of RM and target ship. Finally, the accelerations of the target ship are predicted using RM and linear SVM. The parameters of the RM are known and conveniently acquired, thus avoiding the modeling process. The SVM has the advantages of fast training, quick simulation, and no overfitting. Testing and validation are conducted using the ship model test data. The test case reveals the practicability of the RF-SVM based modeling method, while the validation cases confirm the generalization ability of the grey box framework.

Słowa kluczowe

EN

ship maneuvering; reference model; linear support vector machine; grey box framework; similarity rule

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2019
• Tom: nr 3
• Strony: 115--127
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Me Bin

• Dalian Maritime University NO. 1 Linghai Road 116026 Dalian China

autor

Sun Licheng

• Dalian Maritime University NO. 1 Linghai Road 116026 Dalian China

autor

Shi Guoyou

• Dalian Maritime University NO. 1 Linghai Road 116026 Dalian China

autor

Liu Xiaodong

• Dalian University of Technology No.2 Linggong Road, Ganjingzi District Dalian City Liaoning Province 116024 Dalian China

Bibliografia

• 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.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).