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Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control
1 Osaka University, Osaka, Japan
ABSTRACT: In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ?virtual window? is introduced. Such consistent teaching data are then used to train two separate multi-layered feed forward neural networks for command rudder and propeller revolution output. After proper training, several known and unknown conditions are tested to judge the effectiveness of the proposed controller using Monte Carlo simulations. After getting acceptable percentages of success, the trained networks are implemented for the free running experiment system to judge the network?s real time response for Esso Osaka 3-m model ship. The network?s behaviour during such experiments is also investigated for possible effect of initial conditions as well as wind disturbances. Moreover, since the final goal point of the proposed controller is set at some distance from the actual pier to ensure safety, therefore a study on automatic tug assistance is also discussed for the final alignment of the ship with actual pier.
REFERENCES
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Ahmed, Y. A. and Hasegawa, K. 2013b. Implementation of Automatic Ship Berthing using Artificial Neural Network for Free Running Experiment. Proc. of the 9th IFAC Conference on Control Applications in Marine Systems, vol. 9, pp.25-30, Osaka, Japan.
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Citation note:
Ahmed Y.A., Hasegawa K.: Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 9, No. 3, doi:10.12716/1001.09.03.15, pp. 417-426, 2015

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