A Cross-Control-Logic and Disturbance-Adaptive Line-Adhering Intelligent Navigation Framework for Autonomous Ships (opens in a new tab)
This paper introduces a navigation framework for autonomous ships that improves path-following in curved routes and under wind or current disturbances. It uses a rough-set classifier to detect disturbances and adjusts PID, LQR, and MPC controllers in real time. A Bézier-based method refines paths to match ship maneuvering limits, while Nomoto and MMG models ensure dynamic feasibility. Simulations demonstrate better precision, robustness, and comfort compared to traditional heading-keeping autopilots.