@article{Ahmed_Hasegawa_2015, author = {Ahmed, Yaseen Adnan and Hasegawa, Kazuhiko}, title = {Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control}, journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation}, volume = {9}, number = {3}, pages = {417-426}, year = {2015}, url = {./Article_Consistently_Trained_Artificial_Ahmed,35,600.html}, 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.}, doi = {10.12716/1001.09.03.15}, issn = {2083-6473}, publisher = {Gdynia Maritime University, Faculty of Navigation}, keywords = {Port Maneuvres, Artificial Neural Network (ANN), Automatic Ship Berthing Control, Ship Berthing, Automatic Ship Berthing, Monte Carlo Simulation, Autonomous Underwater Vehicle (AUV), Teaching Data Creation} }