{
  "abstract": "A nonfatal propulsion fault can invalidate the nominal ascent trajectory while leaving sufficient vehicle capability to reach the original orbit or a degraded target. Deterministic replanning can restore nominal feasibility, but residual uncertainty in the identified propulsion capability may still lead to path-constraint violations and terminal injection dispersion. This paper develops a fast chance-constrained stochastic ascent trajectory replanner that explicitly accounts for persistent propulsion uncertainty. The uncertainty model combines persistent random uncertainties shared across the prediction horizon with interval-wise independent disturbances. For a fixed nominal trajectory, system level synthesis is used to parameterize the closed-loop responses. A Cholesky-based transformation of the system responses converts the resulting cross-time correlated feedback design problem into column-wise affine recursions that share a common Riccati recursion. A task-oriented terminal weight is further derived from the minimum-energy post-injection orbital correction in phase-free modified equinoctial elements, thereby relating terminal covariance shaping directly to correction demand. The stochastic replanning problem is solved by alternating deterministic trajectory replanning and feedback controller design, with covariance-based chance-constraint backoffs coupling the two updates. Nonlinear Monte Carlo simulations demonstrate reduced equivalent correction demand, effective chance-constraint satisfaction, and fast online replanning over a range of uncertainty levels and fault conditions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.02989",
  "author_total": 6,
  "authors": [
    {
      "name": "Dai Shen"
    },
    {
      "name": "Chen Yang"
    },
    {
      "name": "Lin Cheng"
    },
    {
      "name": "Hongbo Zhang"
    },
    {
      "name": "Hongtao Zheng"
    },
    {
      "name": "Shengping Gong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cbrs0yzdt0mtq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.02989",
  "fulltext_url": null,
  "id": "cbrs0yzdt0mtq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.02989",
  "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",
      "math.OC"
    ],
    [
      "openalex",
      "2202|Spacecraft Dynamics and Control"
    ]
  ],
  "openalex": "W7220419234",
  "pdf_url": "https://arxiv.org/pdf/2610.02989",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cbrs0yzdt0mtq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:50Z",
      "native_id": "2610.02989",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.02989"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "System Level Synthesis for Fast Chance-Constrained Post-Fault Ascent Replanning under Persistent Propulsion Uncertainty",
  "title_text": "System Level Synthesis for Fast Chance-Constrained Post-Fault Ascent Replanning under Persistent Propulsion Uncertainty",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:22Z",
  "url": "https://research.draconitic.com/paper/cbrs0yzdt0mtq",
  "venue": "arXiv",
  "versions": []
}