Autonomous surface vehicles (ASVs) are 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 augmented by an online learning module to compensate for these disturbances in real time. This design enables adaptive and robust tracking control while maintaining computational efficiency. Extensive evaluations in 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（ASV）易受风、浪等环境扰动影响，使得在动态海洋环境中实现精确轨迹跟踪成为一项持续挑战。本文提出一种用于未知扰动下海洋航行器轨迹跟踪的高效控制器，其核心在于结合李群上的凸误差状态模型预测控制（MPC）与在线学习模块，以实时补偿此类扰动。该设计在保持计算效率的同时，实现了自适应且鲁棒的跟踪控制。在Virtual RobotX（VRX）仿真环境及真实外场实验中的大量评估表明，与现有方法相比，本方法在多种扰动场景下均能实现更优的跟踪精度。