Ship roll damping via direct inverse neural network control system

Abstract

Undesired roll motions of surface vessels under sea way have negative influence on passengers, crew members, equipment and cargo. Damping roll motions is a complex problem due to unpredictable and irregular nature of marine environmental disturbances that induce roll motions. There are several stabilizing systems regarding roll motions such as gyrostabilizers, anti-roll tanks, active fins and rudder roll stabilizers. Among all types of roll stabilizers or roll damping systems active fins practically represents the most effective roll damping technique and yet the most commonly used roll stabilizer. Assuming that the longitudinal location of fins corresponds to the vessel center of gravity a one degree of freedom nonlinear model can be used to identify ship dynamics. In this paper the performance and robustness of a proposed control system based on neural network approach applied to active fins that takes into account roll response spectral characteristics transitions due to not only varying sea state but also changing sailing conditions is assessed. The suggested control system achieves robust performance with adequate roll damping effect that ranges from 50 to 65% roll reduction according to severity of weather conditions and sailing status.