System Level Synthesis for Fast Chance-Constrained Post-Fault Ascent Replanning under Persistent Propulsion Uncertainty
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.
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BibTeX
@misc{shen2026system,
title = {{System Level Synthesis for Fast Chance-Constrained Post-Fault Ascent Replanning under Persistent Propulsion Uncertainty}},
author = {Dai Shen and Chen Yang and Lin Cheng and Hongbo Zhang and Hongtao Zheng and Shengping Gong},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.02989},
eprint = {2610.02989},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.02989},
}
RIS
TY - UNPB
TI - System Level Synthesis for Fast Chance-Constrained Post-Fault Ascent Replanning under Persistent Propulsion Uncertainty
AU - Dai Shen
AU - Chen Yang
AU - Lin Cheng
AU - Hongbo Zhang
AU - Hongtao Zheng
AU - Shengping Gong
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.02989
UR - https://arxiv.org/abs/2610.02989
AB - 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.
ER -