|Table of Contents|

Citation:
　Weilin Luo,Zhicheng Zhang.Modeling of Ship Maneuvering Motion Using Neural Networks[J].Journal of Marine Science and Application,2016,(4):426-432.[doi:10.1007/s11804-016-1380-8]
Click and Copy

Modeling of Ship Maneuvering Motion Using Neural Networks

Info

Title:
Modeling of Ship Maneuvering Motion Using Neural Networks
Author(s):
Weilin Luo1; 2;  Zhicheng Zhang1
Affilations:
Author(s):
Weilin Luo1; 2;  Zhicheng Zhang1
1. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China;
2. Fujian Province Key Laboratory of Structural Performances in Ship and Ocean Engineering, Fuzhou 350116, China
Keywords:
ship maneuvering|response models|mathematical modeling group model|system identification|neural networks
分类号:
-
DOI:
10.1007/s11804-016-1380-8
Abstract:
In this paper, Neural Networks (NNs) are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverability indices and linear non-dimensional hydrodynamic derivatives in the models are identified by using two-layer feed forward NNs. The stability of parametric estimation is confirmed. Then, the ship maneuvering motion is predicted based on the obtained models. A comparison between the predicted results and the model test results demonstrates the validity of the proposed modeling method.

References:

Abkowitz MA, 1980. Measurement of hydrodynamic characteristic from ship maneuvering trials by system identification. Transactions of Society of Naval Architects and Marine Engineers, 88, 283-318.
Bhattacharyya SK, Haddara MR, 2006. Parametric identification for nonlinear ship manoeuvring. Journal of Ship Research, 50(3), 197-207.
Chen Y, Song Y, Chen M, 2010. Parameters identification for ship motion model based on particle swarm optimization. Kybernetes, 39(6), 871-880.
DOI: 10.1108/03684921011046636
Ebada A, Abdel-Maksoud M, 2005. Applying artificial intelligence (A.I.) to predict the limits of ship turning manoeuvres. Year Book of Shipbuilding Institute, 99, 132-139.
Haddara MR, Wang Y, 1999. Parametric identification of maneuvering models for ships. International Shipbuilding Progress, 46(445), 5-27.
DOI: 10.3233/ISP-1999-4644501
Herrero ER, Gonzalez FJV, 2012. Two-step identification of non-linear manoeuvring models of marine vessels. Ocean Engineering, 53, 72-82.
DOI: 10.1016/j.oceaneng.2012.07.010
Hess D, Faller W, Lee J, Fu T, Ammeen E, 2006. Ship maneuvering simulation in wind and waves: a nonlinear time-domain approach using recursive neural networks. Proceedings of the 26th Symposium on Naval Hydrodynamics, Rome.
Holzhüter T, 1989. Robust identification in an adaptive track controller for ships. Proceedings of the 3rd IFAC Symposium on Adaptive Systems in Control and Signal Processing, Glasgow, 275-280.
IMO, 2002. Standards for ship manoeuvrability. Resolution MSC.137(76). International Maritime Organization, Lodon.
Källström CG, Åström KJ, 1981. Experiences of system identification applied to ship steering. Automatica, 17(1), 187-198.
DOI: 10.1016/0005-1098(81)90094-7
Lewis FL, Liu K, Yesildirek A, 1995. Neural net robot controller with guaranteed tracking performance. IEEE Transactions on Neural Networks, 6(3), 703-715.
DOI: 10.1109/72.377975
Luo WL, Moreira L, Guedes Soares C, 2014. Manoeuvring simulation of catamaran by using implicit models based on support vector machines. Ocean Engineering, 82, 150-159.
DOI: 10.1016/j.oceaneng.2014.03.008
Luo WL, Guedes Soares C, Zou ZJ, 2016. Parameter identification of ship manoeuvring model based on support vector machines and particle swarm optimization. Journal of Offshore Mechanics Arctic Engineering, 138(3), 031101-1-031101-8.
DOI: 10.1115/1.4032892
Moreira L, Guedes Soares C, 2003a. Dynamic model of manoeuvrability using recursive neural networks. Ocean Engineering, 30(13), 1669-1697.
DOI: 10.1016/S0029-8018(02)00147-6
Moreira L, Guedes Soares C, 2003b. Comparison between manoeuvring trials and simulations with recursive neural networks. Ship Technology Research, 50, 77-84.
DOI: 0.1179/str.2003.50.2.004
Newman JN, 1977. Marine hydrodynamics. M.I.T Press, Cambridge, USA.
Nomoto K, Tagushi T, Honda K, Hirano S, 1957. On steering qualities of ships. International Shipbuilding Progress, 4(35), 354-370.
Qu ZH, Dawson DM, 1995. Robust tracking control of robot manipulators. IEEE Press, Piscataway, USA.
Rajesh G, Bhattacharyya SK, 2008. System identification for nonlinear maneuvering of large tankers using artificial neural network. Applied Ocean Research, 30(4), 256-263.
DOI: 10.1016/j.apor.2008.10.003
Sutulo S, Guedes Soares C, 2014. 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
Van Amerongen J, 1984. Adaptive steering of ships - a model reference approach. Automatica, 20(1), 3-14.
DOI: 10.1016/0005-1098(84)90060-8
Yoon HK, Rhee KP, 2003. Identification of hydrodynamic derivatives in ship maneuvering equations of motion by estimation-before-modeling technique. Ocean Engineering, 30, 2379-2404.
DOI: 10.1016/S0029-8018(03)00106-9
Zhou WW, Blanke M, 1989. Identification of a class of nonlinear state-space models using RPE techniques. IEEE Transactions on Automatic Control, 34(3), 312-316.
DOI: 10.1109/9.16421

Memo

Memo:
Received date:2015-12-31;Accepted date:2016-8-4。
Foundation item:Partially Supported by the Special Item for the Fujian Provincial Department of Ocean and Fisheries (No. MHGX-16), and the Special Item for Universities in Fujian Province by the Education Department (No. JK15003)
Corresponding author:Weilin Luo
Last Update: 2016-11-24