{
  "abstract": "Reliable formation tracking of unmanned surface vehicle (USV) clusters is challenging when environmental disturbances, noisy measurements, actuator saturation, command-bandwidth limits, and imperfect communications coexist in the same closed loop. This paper proposes a frequency-aware robust output-feedback formation-tracking framework that integrates command-filtered backstepping, sliding-mode control, a saturation-protected high-gain observer, and momentum-based disturbance estimation. The command filter is embedded in the recursive backstepping design and combined with auxiliary compensation dynamics, so that the filter-induced phase lag and magnitude attenuation are explicitly characterized and exactly cancelled in the nominal tracking-error recursion, while their residual influence on the complete closed loop is compensated to within a bounded ultimate error. The high-gain observer reconstructs unmeasured velocities from noisy pose measurements, and its saturated outputs are used in all executable control laws to prevent observer peaking from being directly injected into the actuator commands. Actuator saturation, communication delays, and packet dropouts are further incorporated into a unified Lyapunov-based stability analysis. Finally, simulations validate the effectiveness and superiority of the proposed control scheme.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mechanical Engineering, Hubei University of Technology",
    "Hubei Key Laboratory of Rail Logistics Technology and Equipment, Hubei Institute of Logistics Technology",
    "Engineering and Technology College of Hubei University of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yuxin Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Xun Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xin Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Zejian Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiaohong Dang"
    },
    {
      "affs": [
        2
      ],
      "name": "Yuxi Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f7bny0y33cqt9.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-65149-5",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-65149-5",
  "id": "f7bny0y33cqt9",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-65149-5",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1705|Distributed Control Multi-Agent Systems"
    ]
  ],
  "openalex": "W7172527353",
  "pdf_url": "https://www.nature.com/articles/s41598-026-65149-5_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/f7bny0y33cqt9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:47Z",
      "native_id": "a8e5cef1adc34201847c3c3b379a1afc",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-65149-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Frequency-aware high-precision formation control of unmanned surface vehicle clusters",
  "title_text": "Frequency-aware high-precision formation control of unmanned surface vehicle clusters",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:47Z",
  "url": "https://research.draconitic.com/paper/f7bny0y33cqt9",
  "venue": "Scientific Reports",
  "versions": []
}