A Neural Network Approach to Control Allocation of Ships for Dynamic Positioning

Abstract

Dynamic Positioning (DP) of ships is a control mode that seeks to maintain a specific position (stationkeeping) or perform low-speed maneuvers. In this paper, a static Neural Network (NN) is proposed for control allocation of an over-actuated ship. The thruster force and commands are measured during a trial run of the simulated vessel to gather data for training of the NN. Then the network is trained and used to transform the virtual force commands from a motion controller into individual thruster commands. A standard Proportional Integral Derivative (PID) controller, using wave-filtered position and heading measurements, is implemented as motion controller for each Degree Of Freedom (DOF) of the ship. For a DP application the controllable DOFs are the translational motion in surge and sway directions, as well as the rotation about its up/down axis. Simulation tests were performed to verify the feasibility of this approach.