Adaptive Neural Heading Control for Roll Reduction of FLNG in Multi-Directional Sea Conditions

Increasing demand for clean fuel technologies along with rising focus toward sustainable energy is expediting the floating liquefied natural gas (FLNG) market growth. This paper investigates roll reduction for a turret moored FLNG vessel in multi-directional seas to improve operability. To achieve roll reduction, the vessel's heading is controlled such that the roll moment due to environmental forces is minimized. An adaptive neural Network (ANN) approximator is introduced to account for uncertainties and un-modeled dynamics. The closed loop signals are proved to be semiglobally uniformly bounded (SGUB) using Lyapunov stability analysis. The capabilities of the proposed heading control and roll reduction effectiveness are demonstrated through time-domain simulations in 3 multi-directional extreme seas. Roll motions are reduced, leading towards larger operation envelops and better productivity.