# Robust Trajectory Tracking of Autonomous Surface Vehicle via Lie Algebraic Online MPC

arXiv: https://arxiv.org/abs/2511.18683 · HTML: https://mlsanity.com/paper/2511.18683/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2025.

## Built on by (most cited first, 2 of 2)

- [CHyLL: Learning Continuous Neural Representations of Hybrid Systems](https://mlsanity.com/paper/2512.10117/) (2025)
- [Embedding Hybrid Systems into Continuous Latent Vector Fields](https://mlsanity.com/paper/2606.10596/) (2026)

## Abstract (arXiv)

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.