AI-Driven Mooring Control for Autonomous Engineering Vessels

Precise station-keeping of construction barges during offshore operations remains a demanding control problem because the underlying dynamics are highly nonlinear and the disturbance environment is seldom known a priori. This work investigates how a learning-based controller can be embedded into the coordinated winch-control architecture of a specialized engineering vessel to deliver accurate positioning in shallow water. Vessels such as rock-dumping platforms and pipe-laying barges routinely rely on a spread of mooring lines to hold station, and the tensions on these lines are, in current industrial practice, still adjusted manually by the winch operator. The scheme proposed here replaces that manual loop with an adaptive neural feedback law synthesized through backstepping, allowing the unknown portions of the ship model and the exogenous environmental loads to be compensated online without requiring prior identification. The 3DOF control wrench produced by the feedback law is then mapped to the physical line tensions through a constrained allocation that respects the unilateral and breaking-load constraints of the spread. The closed-loop system is shown to be semi-globally uniformly ultimately bounded (SGUUB) in the Lyapunov sense, and its performance is benchmarked against a conventional PD regulator and a nominal model-based design through simulation of a full-scale rock installation barge. When the model-based baseline is given the nominal plant, it attains the cleanest tracking; the proposed neural law achieves comparable steady-state accuracy without requiring prior identification of the hydrodynamic coefficients. A model-free deep reinforcement learning (PPO) controller is additionally benchmarked under irregular (JONSWAP) seas; it attains bounded sub-metre station-keeping without any model knowledge, on par with the PD baseline but less precise than the model-based and adaptive-neural laws.