Artificial Neural Network Based Automatic Ship Berthing Combining PD Controlled Side Thrusters

Manoeuvring ship during berthing has always required vast experience, skill and knowledge to provide desired necessary actions. Presence of environmental disturbances as well as decreased manoeuvrability in low speed often makes the whole procedure so sophisticated that even slight mistake may results catastrophic disaster. By knowing the fact that Artificial Neural Network (ANN) has the ability to replicate human brains and good enough for controlling such multi-input multi-out nonlinear system, at the beginning of this research, consistent teaching data are created using Non Linear Programing (NPL) method and a new concept named ‘virtual window’ is introduced. Later on, considering gust wind disturbances, two separate multilayer feed forward networks are trained using back propagation technique for command rudder and propeller revolution output. After being successful in simulation works, real time berthing experiments are carried out for Esso Osaka 3-m model where the ship is planned to successfully stop within a distance of 1.5L from actual pier to ensure safety. Finally, as a current status, PD controlled side thrusters are included in order to shake hand with current controller to align the ship with pier considering wind up to 1.5 m/s for model ship. Keywords—ship berthing, artificial neural network, PD controller, nonlinear programming language, side thruster