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Unlike traditional path-following methods, this paper develops a parallel control framework to investigate the path-following behavior of autonomous surface ships (ASVs) in the presence of unknown disturbances and model uncertainties. Specifically, a high-order learning extended state observer is proposed to construct a digital-twin ASV system, enabling precise depiction of the motion characteristics of the actual ASV system. Based on the states and unknown total disturbances estimated by the digital-twin ASV system, a parallel path-following controller is developed to actively guide the ASV to achieve path-following task. Simulation results validate the efficacy of the proposed parallel path-following control scheme for the fully-actuated ASV.

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Parallel Path-Following Control of Fully-Actuated Autonomous Surface Vehicles Based on a High-Order Learning Extended State Observer

  • 会议论文

  • 发表日期：

    2025年

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  • Jiaxue Xu,
  • Haodong Liu,
  • Nan Gu,
  • Lu Liu,
  • Zhouhua Peng

摘要

Unlike traditional path-following methods, this paper develops a parallel control framework to investigate the path-following behavior of autonomous surface ships (ASVs) in the presence of unknown disturbances and model uncertainties. Specifically, a high-order learning extended state observer is proposed to construct a digital-twin ASV system, enabling precise depiction of the motion characteristics of the actual ASV system. Based on the states and unknown total disturbances estimated by the digital-twin ASV system, a parallel path-following controller is developed to actively guide the ASV to achieve path-following task. Simulation results validate the efficacy of the proposed parallel path-following control scheme for the fully-actuated ASV.