Autonomous surface vehicles (ASVs) are easily influenced by environmental disturbances such as wind and waves, making accurate trajectory tracking a persistent challenge in dynamic marine conditions. In this paper, we propose an efficient controller for trajectory tracking of marine vehicles under unknown disturbances by combining a convex error-state MPC on the Lie group with an online learning module to compensate for these disturbances in real time. This design enables adaptive and robust control while maintaining computational efficiency. Extensive evaluations in numerical simulations, the Virtual RobotX (VRX) simulator, and real-world field experiments demonstrate that our method achieves superior tracking accuracy under various disturbance scenarios compared with existing approaches.

翻译：自主水面航行器（ASVs）易受风浪等环境扰动影响，在动态海洋环境中实现精确轨迹跟踪是一项持续挑战。本文提出一种针对未知扰动下海洋航行器轨迹跟踪的高效控制器，通过将李群上的凸误差状态模型预测控制与在线学习模块相结合，实时补偿扰动。该设计在保持计算效率的同时实现了自适应鲁棒控制。在数值仿真、Virtual RobotX（VRX）仿真器及真实外场实验中的广泛评估表明，相较于现有方法，本方法在多种扰动场景下均实现了更优的跟踪精度。