A multivariable neural controller for automatic ship berthing

This article describes the development and application of a multivariable neural controller for automatic ship berthing. Following a brief review of various methods employed in automatic ship control, an online trained, backpropagation-based neural network controller is presented. The principal intention is to take advantage of the learning ability of neural networks, and to derive an autonomous neural control algorithm which is independent of the mathematical model of the ship. The proposed neural network controller is designed to adjust its parameters online from a direct evaluation of performance accuracy, thereby eliminating the need for off-line training and a “trainer” associated with supervised control. In addition, the nonlinearity of the rudder and the transfer lag of the propeller have been considered in the system design to increase the realism of the simulation. A series of simulation studies, which include wind disturbances and shallow water effects, have been undertaken to demonstrate the adaptive features and the robust performance of the proposed neural control scheme