Authors

Chengxing Lv, Ying Zhang, Zichen Wang, Jian Chen, Zhibo Yang, Haisheng Yu

Abstract

This paper proposes a novel event-triggered energy-based controller for Unmanned Surface Vessels (USVs) operating in complex scenarios, integrating reinforcement learning techniques with an energy-based framework. Model uncertainties are captured via actor-critic neural networks (NNs), where actor NNs generate control actions and critic NNs assess their performance. To address disturbances, a self-learning nonlinear disturbance observer with an adaptive learning factor is developed, enhancing the accuracy of disturbance estimation. A state-error port-controlled Hamiltonian (PCH) strategy ensures trajectory tracking, complemented by variable damping techniques to optimize the closed-loop system’s dynamic response. The design incorporates event-triggered mechanisms and adaptive control methods to ensure boundedness of all closed-loop signals. Stability analysis demonstrates convergence of the tracking error to a neighborhood of the origin, and simulation results validate the controller’s feasibility and efficacy.

Keywords

Actor-critic neural networks; Event-triggered mechanism; Unmanned surface vessel; Energy based framework

Citation

BibTeX

@article{Lv_2025,
  title={{Reinforcement learning event-triggered energy-based control for unmanned surface vessel with disturbances}},
  volume={329},
  ISSN={0029-8018},
  DOI={10.1016/j.oceaneng.2025.121132},
  journal={Ocean Engineering},
  publisher={Elsevier BV},
  author={Lv, Chengxing and Zhang, Ying and Wang, Zichen and Chen, Jian and Yang, Zhibo and Yu, Haisheng},
  year={2025},
  pages={121132}
}

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References