Sea-Trial Validated Adaptive Control for Uncrewed Surface Vehicles: Integrating Robust Line-of-Sight With Dual-Mode Model Predictive Control

This paper addresses path following control for underactuated unmanned surface vehicle (USV) equipped with towed side-scan sonars, where dynamic coupling and nonlinear disturbances pose significant challenges. Existing methods often lack experimental validation in realistic sea trials. To overcome these problems, we propose an adaptive robust line-of-sight model predictive control (ARLOS-MPC) scheme integrated with dual-mode artificial bee colony (ABC) optimization. Our approach enhances robustness against kinematic discrepancies and hydrodynamic disturbances under operational constraints. Key innovations include an adaptive Line-of-Sight (LOS) method employing sliding mode concepts with a speed-dependent lookahead distance, a dual-mode predictive control algorithm that incorporates path geometry and environmental disturbances via curvature change rates and dynamic weights, and a collaborative mechanism coupling step size and prediction horizon for synergistic global-local search. Empirical validation through simulations and sea trials under varied conditions shows that ARLOS-MPC reduces path following errors, accelerates convergence, improves robustness, and enhances adaptability, outperforming existing methods in computational efficiency and error reduction.