Research on Autonomous Collaborative Berthing of Multi-Tug and Ultra-Large Under-Actuated Vessels

In the autonomous berthing of ultra-large under-actuated vessels, the combined effects of low-speed maneuvering and shallow water conditions introduce strong nonlinear hydrodynamic characteristics which cannot be accurately captured by conventional linear models. To address this issue, a nonlinear maneuvering model is adopted to more precisely describe vessel dynamics. A 300 m class ultra-large tanker, Esso Bernicia (190,000 dwt), is selected as the case study to ensure the applicability of the proposed method to vessels with high inertia and limited maneuverability. Furthermore, to better reflect realistic berthing conditions, environmental disturbances and hydrodynamic parameters are modeled based on the experiments and statistical analysis conducted by OCIMF, and shallow water effects are incorporated to account for increased resistance in confined port areas.In contrast to existing studies that rely solely on tug assistance, this work integrates propeller–rudder control of the vessel into the berthing process. The coordinated use of onboard propulsion and tug forces significantly enhances maneuverability and operational efficiency. However, these modeling improvements result in a centralized multi-input–multi-output (MIMO) nonlinear system with increased complexity, higher-dimensional control inputs, and stronger coupling effects, posing significant challenges for control design. To address these challenges, a phased berthing control strategy is proposed. The overall multi-objective optimization problem is decomposed into stage-wise sub-problems to reduce computational complexity. In addition, a tailored berthing trajectory and stage-dependent cost functions are designed to facilitate convergence and improve computational efficiency. Simulation results demonstrate that the proposed method achieves safe, stable, and efficient autonomous berthing, with improved convergence performance and enhanced control effectiveness under complex environmental conditions.