Online Residual Model Learning for Model Predictive Control of Autonomous Surface Vehicles in Real-World Environments

Abstract

Model predictive control (MPC) relies on an accurate dynamics model to achieve precise and safe robot operation. In complex and dynamic aquatic environments, developing an accurate model that captures hydrodynamic details and accounts for environmental disturbances like waves, currents, and winds is challenging for aquatic robots. In this paper, we propose an online residual model learning framework for MPC, which leverages approximate models to learn complex unmodeled dynamics and environmental disturbances in dynamic aquatic environments. We integrate offline learning from previous simulation experience with online learning from the robot’s real-time interactions with the environments. These three components—residual modeling, offline learning, and on-line learning—enable a highly sample-efficient learning process, allowing for accurate real-time inference of model dynamics in complex and dynamic conditions. We further integrate this online learning residual model into a nonlinear model predictive controller, enabling it to actively choose the optimal control actions that optimize the control performance. Extensive simulations and real-world experiments with an autonomous surface vehicle demonstrate that our residual model learning MPC significantly outperforms conventional MPCs in dynamic field environments.