Scholarlink

<p>Performing rendezvous and proximity operations (RPO) is an important part of the life cycle of many satellite missions. Although several methods have been proposed to develop guidance solutions for RPO problems, they are all based on a nominal description of the dynamical system. In practice, however, various uncertainties must be managed. For example, un-managed uncertainty in the mean motion or orbital velocity can have a large impact on the final position and velocity of the satellite. To obtain a robust guidance solution for such RPO problems and reduce the requirements on feedback, we employ unscented trajectory optimization which uses the unscented transformation as a means to minimize the variance about the terminal target. We show that the standard (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(2n+1\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>2</mn> <mi>n</mi> <mo>+</mo> <mn>1</mn> </mrow> </math></EquationSource> </InlineEquation>) unscented transform may not adequately capture the maneuver statistics, prompting the use of higher-order (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(4n+1\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>4</mn> <mi>n</mi> <mo>+</mo> <mn>1</mn> </mrow> </math></EquationSource> </InlineEquation>) unscented transformations for improving the reliability of uncertain RPO guidance solutions.</p>

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RPO guidance under uncertainty using higher-order unscented trajectory optimization

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Shambo Bhattacharjee,
  • Mark Karpenko

摘要

Performing rendezvous and proximity operations (RPO) is an important part of the life cycle of many satellite missions. Although several methods have been proposed to develop guidance solutions for RPO problems, they are all based on a nominal description of the dynamical system. In practice, however, various uncertainties must be managed. For example, un-managed uncertainty in the mean motion or orbital velocity can have a large impact on the final position and velocity of the satellite. To obtain a robust guidance solution for such RPO problems and reduce the requirements on feedback, we employ unscented trajectory optimization which uses the unscented transformation as a means to minimize the variance about the terminal target. We show that the standard ( \(2n+1\) 2 n + 1 ) unscented transform may not adequately capture the maneuver statistics, prompting the use of higher-order ( \(4n+1\) 4 n + 1 ) unscented transformations for improving the reliability of uncertain RPO guidance solutions.