Meta-Learning Based USV Dynamics Residual Modeling for MPC Trajectory Tracking under Different Sea States
Abstract
Unmanned Surface Vehicles (USVs) encounter varying disturbances across different sea states, making it difficult to achieve stable trajectory tracking based solely on nominal physical models. This limitation has motivated the development of disturbance observer-based compensation schemes. However, existing disturbance observers often suffer from slow updating rates, resulting in insufficient adaptability. Therefore, this paper presents a meta-learning based dynamic residual modeling method. To facilitate rapid adaptation to disturbances, Model-Agnostic Meta-Learning is employed for pre-training. Through dual-loop gradient updates, the meta-learned initial parameters for the residual dynamics model are obtained. Subsequently, the residual model is integrated with the nominal model and embedded into the model predictive control framework, utilizing few-shot observation data to execute online fine-tuning of the residual network, thereby continuously correcting the dynamic residuals induced by time-varying sea states. Simulation experiments conducted in Gazebo VRX demonstrate that the proposed method maintains stable trajectory tracking in time-varying environments, significantly enhancing the overall disturbance rejection capability and robustness of the USV in complex sea states.