Papers
Topics
Authors
Recent
Search
2000 character limit reached

Parameter fine-tuning method for MMG model using real-scale ship data

Published 7 Dec 2023 in eess.SY, cs.RO, and cs.SY | (2312.04224v1)

Abstract: In this paper, a fine-tuning method of the parameters in the MMG model for the real-scale ship is proposed. In the proposed method, all of the arbitrarily indicated target parameters of the MMG model are tuned simultaneously in the framework of SI using time series data of real-sale ship maneuvering motion data to steadily improve the accuracy of the MMG model. Parameter tuning is formulated as a minimization problem of the deviation of the maneuvering motion simulated with given parameters and the real-scale ship trials, and the global solution is explored using CMA-ES. By constraining the exploration ranges to the neighborhood of the previously determined parameter values, the proposed method limits the output in a realistic range. The proposed method is applied to the tuning of 12 parameters for a container ship with five different widths of the exploration range. The results show that, in all cases, the accuracy of the maneuvering simulation is improved by applying the tuned parameters to the MMG model, and the validity of the proposed parameter fine-tuning method is confirmed.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (48)
  1. Ship hydrodynamics - steering and manoeuvrability. Technical Report. Hydro- and Aerodynamics Laboratory. URL: https://repository.tudelft.nl/islandora/object/uuid:d511bd6b-ca2e-4f10-ad9f-6c881eb1e9f8?collection=research.
  2. Measurement of hydrodynamic characteristics from ship maneuvering trials by system identification. Transactions of Society of Naval Architects and Marine Engineers 88, 283–318. URL: https://trid.trb.org/view/157366.
  3. On the prediction method for maneuverability of a full scale ship. Journal of the Japan Society of Naval Architects and Ocean Engineers 3, 157–165. doi:10.2534/JJASNAOE.3.157. (in Japanese).
  4. Estimating maneuvering coefficients using system identification methods with experimental, system-based, and cfd free-running trial data. Ocean Engineering 51, 63–84. doi:10.1016/j.oceaneng.2012.05.001.
  5. A restart cma evolution strategy with increasing population size, in: 2005 IEEE Congress on Evolutionary Computation, IEEE. pp. 1769–1776. doi:10.1109/CEC.2005.1554902.
  6. Turning and course keeping qualities of ships. Transactions SNAME .
  7. Math-data integrated prediction model for ship maneuvering motion. Ocean Engineering 285, 115255. doi:10.1016/J.OCEANENG.2023.115255.
  8. Handbook of Marine Craft Hydrodynamics and Motion Control. John Wiley and Sons. doi:10.1002/9781119994138.
  9. The CMA Evolution Strategy: A Comparing Review, in: Towards a New Evolutionary Computation. Springer Berlin Heidelberg, pp. 75–102. doi:10.1007/3-540-32494-1_4.
  10. Principled design of continuous stochastic search: From theory to practice, in: Principles of System Identification. Springer, pp. 145–180. doi:10.1007/978-3-642-33206-7__8.
  11. Nonparametric modeling of ship maneuvering motion based on self-designed fully connected neural network. Ocean Engineering 251, 111113. doi:10.1016/J.OCEANENG.2022.111113.
  12. Hydrodynamic derivatives on ship manoeuvring. International Shipbuilding Progress 28, 112–125. doi:10.3233/ISP-1981-2832103.
  13. Fundamental study on estimation of system model for berthing and unberthing of full scale ship, in: Conference proceedings, the Japan Society of Naval Architects and Ocean Engineers, pp. 2022A–OS1–5. URL: https://www.jstage.jst.go.jp/article/conf/35/0/35_29/_article/-char/en. (in Japanese).
  14. Estimation of hydrodynamic coefficients from results of real ship sea trials. Polish Maritime Research 25, 65–72. doi:10.2478/pomr-2018-0133.
  15. Active disturbance rejection with sliding mode control based course and path following for underactuated ships. Mathematical Problems in Engineering 2013. doi:10.1155/2013/743716.
  16. Modeling of ship maneuvering motion using neural networks. Journal of Marine Science and Application 15, 426–432. doi:10.1007/S11804-016-1380-8/METRICS.
  17. Parametric identification of ship maneuvering models by using support vector machines. Journal of Ship Research 53, 19–30. URL: https://onepetro.org/JSR/article-abstract/53/01/19/175116/Parametric-Identification-of-Ship-Maneuvering?redirectedFrom=fulltext.
  18. Application of optimal control theory based on the evolution strategy (cma-es) to automatic berthing. Journal of Marine Science and Technology 25, 221–233. doi:10.1007/s00773-019-00642-3.
  19. Parameter identification of ship motion mathematical model based on full-scale trial data. International Journal of Naval Architecture and Ocean Engineering 14, 100437. doi:10.1016/j.ijnaoe.2022.100437.
  20. Development of a mathematical model for harbor-maneuvers to realize modeling automation. arXiv:2302.10459.
  21. System parameter exploration of ship maneuvering model for automatic docking / berthing using cma-es. Journal of Marine Science and Technology 27, 1065–1083.
  22. Optimization on planning of trajectory and control of autonomous berthing and unberthing for the realistic port geometry. Ocean Engineering 245, 110390. doi:10.1016/j.oceaneng.2021.110390.
  23. Dynamic model of manoeuvrability using recursive neural networks. Ocean Engineering 30, 1669--1697. doi:10.1016/S0029-8018(02)00147-6.
  24. Course stability of ships. Journal of Zosen Kiokai 77, 69--90. URL: https://www.jstage.jst.go.jp/article/jjasnaoe1903/1955/77/1955_77_69/_pdf. (in Japanese).
  25. On the measurement of added mass and added moment of inertia for ship motions. Journal of Zosen Kiokai 1959, 83--92. doi:10.2534/jjasnaoe1952.1959.83.
  26. On the steering qualities of ships. International Shipbuilding Progress 4, 354--370.
  27. On the mathematical model of manoeuvring motion of ships. International Shipbuilding Progress 25, 306--319. doi:10.3233/ISP-1978-2529202.
  28. Maneuvering simulations at large drift angles of a ship with a flapped rudder. Applied Ocean Research 135, 103567. doi:10.1016/J.APOR.2023.103567.
  29. Neural network identification of marine ship dynamics. IFAC Proceedings Volumes 46, 191--196. doi:10.3182/20130918-4-JP-3022.00018.
  30. System identification for nonlinear maneuvering of large tankers using artificial neural network. Applied Ocean Research 30, 256--263. doi:10.1016/J.APOR.2008.10.003.
  31. Non-parametric dynamic system identification of ships using multi-output gaussian processes. Ocean Engineering 166, 26--36. doi:10.1016/J.OCEANENG.2018.07.056.
  32. Modified box constraint handling for the covariance matrix adaptation evolution strategy, in: Genetic and evolutionary computation conference, Association for Computing Machinery (ACM). pp. 183--184. doi:10.1145/3067695.3075986.
  33. Identification of kvlcc2 manoeuvring parameters for a modular-type mathematical model by rans method with an overset approach. Ocean Engineering 188, 106257. doi:10.1016/J.OCEANENG.2019.106257.
  34. An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests. Ocean Engineering 79, 10--25. doi:10.1016/j.oceaneng.2014.01.007.
  35. Application of an offline identification algorithm for adjusting parameters of a modular manoeuvring mathematical model. Ocean Engineering 279, 114328. doi:10.1016/J.OCEANENG.2023.114328.
  36. Ship trajectory planning method for reproducing human operation at ports. Ocean Engineering 266, 112763. URL: https://doi.org/10.1016/j.oceaneng.2022.112763.
  37. On neural network identification for low-speed ship maneuvering model. Journal of Marine Science and Technology 27, 772--785. doi:10.1007/s00773-021-00867-1.
  38. Non-parameterized ship maneuvering model of deep neural networks based on real voyage data-driven. Ocean Engineering 284, 115162. doi:10.1016/J.OCEANENG.2023.115162.
  39. Kernel-based support vector regression for nonparametric modeling of ship maneuvering motion. Ocean Engineering 216, 107994. doi:10.1016/J.OCEANENG.2020.107994.
  40. Dynamic model identification of unmanned surface vehicles using deep learning network. Applied Ocean Research 78, 123--133. doi:10.1016/J.APOR.2018.06.011.
  41. System identification of ship dynamic model based on gaussian process regression with input noise. Ocean Engineering 216, 107862. doi:10.1016/J.OCEANENG.2020.107862.
  42. Introduction of mmg standard method for ship maneuvering predictions. J Mar Sci Technol 20, 37--52. doi:10.1007/s00773-014-0293-y.
  43. Identification of hydrodynamic coefficients in ship maneuvering equations of motion by estimation-before-modeling technique. Ocean Engineering 30, 2379--2404. doi:10.1016/S0029-8018(03)00106-9.
  44. Experimental and numerical investigations of advancing speed effects on hydrodynamic derivatives in mmg model, part i: Xvv,yv,nv. Ocean Engineering 179, 67--75. doi:10.1016/j.oceaneng.2019.03.019.
  45. Ship nonlinear-feedback course keeping algorithm based on mmg model driven by bipolar sigmoid function for berthing. International Journal of Naval Architecture and Ocean Engineering 9, 525--536. doi:10.1016/J.IJNAOE.2017.01.004.
  46. Black-box modeling of ship manoeuvring motion based on feed-forward neural network with chebyshev orthogonal basis function. Journal of Marine Science and Technology 18, 42--49. doi:10.1007/S00773-012-0190-1/FIGURES/5.
  47. Soft actor–critic based active disturbance rejection path following control for unmanned surface vessel under wind and wave disturbances. Ocean Engineering 247, 110631. doi:10.1016/J.OCEANENG.2022.110631.
  48. Identification of ship steering dynamics. Automatica 12, 9--22. doi:10.1016/0005-1098(76)90064-9.
Citations (4)

Summary

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Collections

Sign up for free to add this paper to one or more collections.