A New-Generation Motion-Control System for Twin-Hull Vessels Using a Neural Optimal Controller

Abstract

A new ride control system using a neural optimal controller (NOC) is developed and applied to improve the heave and pitch motion responses of two twin-hull vessels operating in regular head seas. A time domain model for the vessel dynamics in the presence of active fin control is used to simulate the vessel and fin motion responses. An online switching procedure is introduced to select among a number of linear quadratic regulator optimal controllers, designed for different operating conditions of the vessel, to improve the system robustness. Although the online switching offered better robustness and performance characteristics, in between switching operating points, it still remained suboptimal. Therefore, an artificial neural network (ANN) controller was developed as an alternative and initially trained to emulate the same level of control at a number of design operating points, as a NOC. The advantage of this novel application is that practical difficulties in applying an online switching procedure are no longer present and, more importantly, the ANN has been capable of nonlinear generalization to give a near optimal solution away from the trained operating conditions.