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

TL;DR

This paper introduces a robust, real-time adaptive control method for autonomous surface vehicles that combines Lie algebraic MPC with online learning to improve trajectory tracking accuracy amidst environmental disturbances.

Contribution

The paper presents a novel integration of Lie algebraic MPC with online disturbance learning for enhanced adaptive control of marine vehicles.

Findings

Superior tracking accuracy in simulations and real-world tests.

Effective disturbance compensation in dynamic marine environments.

Maintains computational efficiency for real-time applications.

Abstract

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.