{
  "abstract": "This work presents an interpretable data-to-control framework for actuator-constrained flutter suppression by combining higher-order dynamic mode decomposition (HODMD), dynamic mode decomposition with control (DMDc), and model predictive control (MPC). The primary case is a pitch-plunge typical section with a trailing-edge flap and rational unsteady aerodynamics. HODMD identifies the dominant frequency and growth rate in subcritical, near-critical, and post-critical regimes from four measured structural channels. With 2% RMS sensor noise, the near-critical growth-rate error is 1.8 x 10^-3 s^-1, substantially lower than with standard DMD without delay embedding. A five-state real reduced-order model is formed from the HODMD subspace, and its flap-input matrix is estimated from a small-amplitude PRBS record using DMDc. For an independent chirp input, the normalized error over all channels is 2.4 x 10^-3. The constrained MPC stabilizes the true post-critical plant for an initial pitch perturbation of 8 degrees with a flap limit of 1 degree, whereas saturated LQR designed from the same model reaches the prescribed validity limit. Three supporting studies assess transferability. SU2 simulations show HODMD frequency recovery consistent with the available spectral resolution; OpenFOAM dynamic-mesh calculations show nearly linear articulated-flap moment authority from 2 to 8 degrees with frequency-dependent gain; and a two-way SU2 Python-FSI case demonstrates bounded disturbance rejection using a proportional-derivative baseline. These studies support modal identification, actuator authority, and closed-loop feasibility, but are not presented as CFD-level MPC validation. The framework provides a reproducible route from early-time measurements to constrained aeroelastic control.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13459",
  "author_total": 1,
  "authors": [
    {
      "name": "Carlos Mendez"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5kk4jpjwx97s3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13459",
  "fulltext_url": null,
  "id": "5kk4jpjwx97s3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13459",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.13459",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5kk4jpjwx97s3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:29Z",
      "native_id": "2609.13459",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13459"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Data-Driven Flutter Suppression via HODMD, DMDcand Constrained MPC",
  "title_text": "Data-Driven Flutter Suppression via HODMD, DMDcand Constrained MPC",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:29Z",
  "url": "https://research.draconitic.com/paper/5kk4jpjwx97s3",
  "venue": "arXiv",
  "versions": []
}