Response Mitigation of Large Floating Platforms Using Artificial Intelligence

Station-keeping for large floating platforms, such as drillships, remains a substantial challenge in rough seas. This study aims to enhance surge and sway control by integrating a data-driven intelligent controller into a conventional dynamic positioning system (DPS). The proposed methodology combines numerical hydrodynamic analysis with a database-assisted control algorithm. Time-domain responses of the platform under irregular waves are generated using diffraction analysis. These responses are stored in a structured database, and the artificial intelligence-assisted controller queries this database in real time. When an exact match is unavailable, control forces are estimated using piecewise cubic Hermite interpolating polynomial interpolation, ensuring smooth and physically consistent predictions. A conventional proportional–integral–derivative-based DPS model is also implemented for comparison. The results indicate a 60% reduction in Root Mean Square surge motion over an 800 s test duration. Power spectral density analysis confirms significant high-frequency attenuation, and cross-correlation reveals a 175 s delay between control action and surge response. A passive roll-damping mechanism is also integrated and activated automatically by the controller. This study demonstrates that database-driven interpolation offers a reliable, computationally efficient alternative to conventionaldynamic positioning control strategies for next-generation drillships.