基于李代数的在线模型预测控制，提升无人船在复杂海况下的轨迹跟踪精度。
Robust Trajectory Tracking of Autonomous Surface Vehicle via Lie Algebraic Online MPC
- 在李群上构建凸误差状态模型预测控制，实时补偿未知干扰
- 虚拟与实测实验均显示跟踪误差显著低于现有方法
- 适合对鲁棒性要求高的海上自主航行系统应用
自主水面航行器（ASVs）受风浪等环境干扰影响，动态海况下实现精确轨迹跟踪仍是长期挑战。本文提出一种高效控制器，通过在李群上构建凸误差状态模型预测控制（MPC），并引入在线学习模块实时补偿未知干扰，实现自适应鲁棒控制，同时保持计算效率。在虚拟机器人竞赛（VRX）仿真平台及真实海域实验中，该方法在多种干扰场景下均展现出优于现有方法的跟踪精度。
原文摘要 · Abstract (English)
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.
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