Online Learning-Enhanced Lie Algebraic MPC for Robust Trajectory Tracking of Autonomous Surface Vehicles

2025-11-23

AI Summary

To address the low trajectory tracking accuracy and poor robustness of autonomous surface vehicles (ASVs) under wind and wave disturbances, this paper proposes a real-time control framework integrating Lie-algebraic error-state model predictive control (MPC) with online learning-based disturbance compensation. The method constructs a convexified error dynamics model directly on the Lie group, enabling efficient MPC optimization; concurrently, a lightweight online learning module adaptively estimates and compensates unknown, time-varying environmental disturbances. This design balances computational efficiency and disturbance adaptability. Extensive evaluations—including numerical simulations, VRX simulator tests, and full-scale sea trials—demonstrate that the proposed approach reduces trajectory tracking error by 23%–37% compared to state-of-the-art methods, significantly enhancing both accuracy and robustness in complex maritime environments.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Achieving robust trajectory tracking for autonomous surface vehicles under environmental disturbances
• Compensating unknown disturbances in real-time through online learning integration
• Maintaining computational efficiency while ensuring adaptive control performance

Innovation

• Lie group MPC for trajectory tracking control
• Online learning module compensates disturbances in real time
• Combines adaptive robustness with computational efficiency