Abstract
A neural network based vessel drift prediction system is proposed. The input vector of the network is formed by ship speed components, the rudder deflection angle, projection of relative wind velocity on the transversal ship axis. A transversal component of the speed (i.e., drift speed) forms the network output. A feedforward neural network with one hidden layer is used as a basic architecture for the drift prediction. Such architecture meets the requirements of the universal approximation theorem. The system takes into account mainly the wind influence for dead reckoning positioning, and the wave influence is not included in the system operation algorithm. The training set is formed by using the MSS toolbox MATLAB software for a container ship for typical motion scenarios with moderate wind velocity. The results of the neural system testing show that the use of the neural network can improve dead reckoning positioning accuracy to a good extent.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley, Hoboken (2021)
Guide for vessel maneuverability (Updated Feb 2017). American Bureau of Shipping, Houston (2017)
Zheleznyakova, A.L.: Physically-based method for real-time modelling of ship motion in irregular waves. Ocean Eng. 195, 106–686 (2020)
Deryabin, V.V.: Neural networks based prediction model for vessel track control. Autom. Control. Comput. Sci. 53(6), 502–510 (2019). https://doi.org/10.3103/S0146411619060038
Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice Hall, New York (2009)
Hornik, K.: Some new results on neural network approximation. Neural Netw. 6(8), 1069–1072 (1993)
Deryabin, V.V., Sazonov, A.E.: A vessel’s dead reckoning position estimation by using of neural networks. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds.) IITI 2018 2018. AISC, vol. 874, pp. 493–502. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01818-4_49
Ebada, A.: Intelligent techniques-based approach for ship maneuvering simulations and analysis (Artificial Neural Networks Application). Doktor-Ing. genehmigte Dissertation, Institute of Ship Technology und Transport Systems, University Duisburg-Essen, Germany (2007)
Knight, B., Maki, K.: Multi-degree of freedom propeller force models based on a neural network and regression. J. Marine Sci. Eng. 8(2), 89 (2020)
Moreira, L., Soares, C.G.: Dynamic model of manoeuvrability using recursive neural networks. Ocean Eng. 30(13), 1669–1697 (2003)
Rajesh, G., Bhattacharyya, S.K.: System identification for nonlinear maneuvering of large tankers using artificial neural network. Appl. Ocean Res. 30, 256–263 (2008)
Skulstad, R., Li, G., Fossen, T.I., Vik, B., Zhang, H.: Dead reckoning of dynamically positioned ships: using an efficient recurrent neural network. IEEE Robot. Autom. Mag. 26(3), 39–51 (2019)
Waclawek, P.: A neural network to identify ship hydrodynamics coefficients. Marine Simulation and Ship Maneuverability. In: Chislett, M.S. (eds.) Proceedings of the international conference, MARSIM 1996, pp. 509–514. Balkema, Rotterdam (1996)
Woo, J., Park, J., Yu, C., Kim, N.: Dynamic model identification of unmanned surface vehicles using deep learning network. Appl. Ocean Res. 78, 123–133 (2018)
Son, K.-H., Nomoto, S.: On the coupled motion of steering and rolling of a high-speed container ship. Naval Archit. Ocean Eng. 20, 73–83 (1982)
Blendermann, W.: Parameter identification of wind loads on ships. J. Wind Eng. Ind. Aerodyn. 51(3), 339–351 (1994)
Yu, H.: Advanced learning algorithms of neural networks. Ph.D. dissertation, Auburn, USA (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Deryabin, V.V. (2022). A Vessel Drift Prediction System on the Basis of a Neural Network. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21). IITI 2021. Lecture Notes in Networks and Systems, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-030-87178-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-87178-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87177-2
Online ISBN: 978-3-030-87178-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)