{
  "abstract": "When dealing with nonlinear systems, classical covariance steering typically propagates uncertainty via first-order linearizations, discarding higher-order Taylor remainders. This truncation causes computed statistical moments to diverge from the true physical state distribution, often leading to chance constraint violations. This paper introduces a discrete-time Sequential Convex Programming (SCP) framework that casts the deterministic one-step nonlinear numerical map as a Linear Stochastic Inclusion. The The Taylor remainder is modeled as a state-independent unstructured uncertainty block, bounded over a uniform envelope. The second-moment tubes are propagated via what we refer to as a robust Stochastic Linear Matrix Inequality (S-LMI) derived from the Petersen's lemma, providing an upper bound on the expected uncentered second moment. Domain-exit risk is bounded analytically via a Markov trace inequality, and spatial chance constraints are enforced via Gauss unimodal second-moment bounds within a Difference-of-Convex program. Simulations on a state-dependent nonlinear dynamic system demonstrate constraint satisfaction.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.27742",
  "author_total": 3,
  "authors": [
    {
      "name": "Man Jun Koh"
    },
    {
      "name": "Hyochoong Bang"
    },
    {
      "name": "SooJean Han"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fn4bx1pwasj1j.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.27742",
  "fulltext_url": null,
  "id": "fn4bx1pwasj1j",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.27742",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2207|Advanced Control Systems Optimization"
    ]
  ],
  "openalex": "W7171991138",
  "pdf_url": "https://arxiv.org/pdf/2607.27742",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fn4bx1pwasj1j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:04Z",
      "native_id": "2607.27742",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.27742"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Chance-Constrained Nonlinear Covariance Control via Robust Linearization Remainder Bounds",
  "title_text": "Chance-Constrained Nonlinear Covariance Control via Robust Linearization Remainder Bounds",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:04Z",
  "url": "https://research.draconitic.com/paper/fn4bx1pwasj1j",
  "venue": "arXiv",
  "versions": []
}