{
  "abstract": "Cycloidal (RV-type) reducers are widely used in industrial robot joints due to their high torque density and low backlash, yet their multi-mesh transmission path produces structured, operating-point-dependent vibration components at the disc-mesh order and associated harmonics and sidebands. This paper presents a real-time, accelerometer-in-the-loop vibration suppression framework that reduces these components online while maintaining tracking performance within the bounds observed in our experiments and operating within predefined safety limits. A tri-axial accelerometer mounted on the reducer housing provides high-bandwidth vibration measurements from which order-synchronous, band-limited metrics are computed in streaming form. These metrics define both the optimization objective and vibration exposure constraints. The control architecture retains the vendor servo loops and adds a vibration-targeted layer combining a low-dimensional anti-resonance parameterization (adaptive notch shaping and narrowband feedforward cancellation aligned with the estimated mesh-order family) with a safety-certified contextual Bayesian optimization module that adapts the parameters as a function of operating context (speed, load proxy, and temperature proxy). A barrier-function-based safety filter runs at the servo rate to enforce constraint handling during operation; its effect is evaluated empirically through logged interventions and constraint statistics. Experimental evaluation on a cycloidal joint testbed across multiple speeds and load levels shows attenuation of the dominant mesh-order vibration component and its harmonics. Tracking accuracy and safety-related signals remained within preset limits during the tested operating conditions. The proposed approach provides a deployable pathway for online vibration minimization in cycloidal robot joints without requiring high-fidelity internal contact models, and its logged parameter trajectories and order-tracked metrics also offer a foundation for condition-aware adaptation over long-term operation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ammar A. Alzaydi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2qqwe94yzydwq.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-58869-1",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-58869-1",
  "id": "2qqwe94yzydwq",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-58869-1",
  "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",
      "2210|Control Systems in Engineering"
    ]
  ],
  "openalex": "W7169713563",
  "pdf_url": "https://www.nature.com/articles/s41598-026-58869-1_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/2qqwe94yzydwq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:57Z",
      "native_id": "48356f6fe66f4fda88a42b6ef4496413",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-58869-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Accelerometer-in-the-loop safe learning control of mesh-order vibrations in cycloidal drives",
  "title_text": "Accelerometer-in-the-loop safe learning control of mesh-order vibrations in cycloidal drives",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:57Z",
  "url": "https://research.draconitic.com/paper/2qqwe94yzydwq",
  "venue": "Scientific Reports",
  "versions": []
}