Two Stage Optimization for Aerocapture Guidance

E.M. Zucchelli, Grani A. Hanasusanto, A. Brandon-Jones, E. Mooij

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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This paper proposes a two-stage optimization approach for aerocapture guidance. In classical entry guidance systems, deterministic optimization is used. Large-scale and short-scale density perturbations may strongly affect the performance of the guidance system, and variations in those are usually not accounted for when computing the command. In this work, perturbations that affect the trajectory at future time-steps are taken into consideration when computing the commanded bank angle. The chosen command is optimal based on a set of possible future perturbations, after the observation of which, a correction can be made. Both two-stage stochastic and two-stage robust optimization are proposed as a solution. In a Monte Carlo analysis consisting of 50 runs, the two-stage robust optimization guidance outperforms an optimal, deterministic, numeric predictor-corrector guidance. Excluding one outlier, also the two-stage stochastic optimization makes the guidance perform better than an optimal deterministic numeric predictor-corrector. With either approach, the computational demands are increased by about thirty times compared to an optimal numeric predictor-corrector. Much of the computation time increase may be reduced by parallelization. On the other hand, the extensive tuning required for the optimal numeric predictor-corrector is not needed for the two-stage optimization guidance, making this approach conceptually more robust. Better modeling of the environment may help further improve the performance. Finally, an approximation to the two-stage robust optimization approach is developed. The guidance has computational requirements only four times larger than those of the optimal numeric predictor-corrector guidance, but can be parallelized into two threads, and, except for two outliers, it offers improved performance.
Original languageEnglish
Title of host publicationAIAA Scitech 2021 Forum
Subtitle of host publication11–15 & 19–21 January 2021Virtual/online event
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages13
ISBN (Electronic)978-1-62410-609-5
Publication statusPublished - 2021
EventAIAA Scitech 2021 Forum - Virtual/online event due to COVID-19 , Virtual, Online
Duration: 11 Jan 202121 Jan 2021

Publication series

NameAIAA Scitech 2021 Forum


ConferenceAIAA Scitech 2021 Forum
CityVirtual, Online

Bibliographical note

Virtual/online event due to COVID-19


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