Trajectory Optimization and Control of Autonomous Surface Ships in Dynamic Environments Using a Hybrid Extended Kalman Filter and Fuzzy Logic System

The dynamic behavior of autonomous surface ships is significantly influenced by environmental factors such as wind and currents, which introduce trajectory divergence and disrupt navigation stability. These disturbances pose significant challenges to maintaining accurate speed over ground (SOG) and course over ground (COG), particularly in real-time maritime operations. Traditional control methods, such as Proportional-Integral-Derivative (PID) controllers, often struggle to adapt to such disturbances, leading to suboptimal corrections and instability in navigation. To address these challenges, we propose a hybrid framework combining Fuzzy Logic Control (FLC) and an Extended Kalman Filter (EKF). The FLC provides real-time adaptability to varying environmental conditions, dynamically correcting SOG and COG based on divergence data. These corrections are integrated into the EKF, which leverages the improved state variables to enhance trajectory prediction accuracy. This dual-layer approach ensures robust control and accurate trajectory alignment even in challenging maritime environments. Comparative evaluations demonstrate the superior performance of the proposed framework. For control accuracy, FLC achieved RMSE values of 1.6648 knots and 28.3427 degrees for SOG and COG, respectively, compared to PID's 5.9102 knots and 55.0976 degrees. Similarly, FLC reduced MAE to 1.3212 knots and 14.4058 degrees for SOG and COG,