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arXiv · 2503.07102

Coordinated Energy-Trajectory Economic Model Predictive Control for Autonomous Surface Vehicles under Disturbances

Zhongqi Deng·Yuan Wang·Jian Huang·Hui Zhang·Yaonan Wang
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Abstract

The paper proposes a novel Economic Model Predictive Control (EMPC) scheme for Autonomous Surface Vehicles (ASVs) to simultaneously address path following accuracy and energy constraints under environmental disturbances. By formulating lateral deviations as energy-equivalent penalties in the cost function, our method enables explicit trade-offs between tracking precision and energy consumption. Furthermore, a motion-dependent decomposition technique is proposed to estimate terminal energy costs based on vehicle dynamics. Compared with the existing EMPC method, simulations with real-world ocean disturbance data demonstrate the controller's energy consumption with a 0.06 energy increase while reducing cross-track errors by up to 18.61. Field experiments conducted on an ASV equipped with an Intel N100 CPU in natural lake environments validate practical feasibility, achieving 0.22 m average cross-track error at nearly 1 m/s and 10 Hz control frequency. The proposed scheme provides a computationally tractable solution for ASVs operating under resource constraints.

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Zhongqi Deng, Yuan Wang, Jian Huang, Hui Zhang, Yaonan Wang. 2025-03-10. Coordinated Energy-Trajectory Economic Model Predictive Control for Autonomous Surface Vehicles under Disturbances. https://arxiv.org/abs/2503.07102

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