Optimization for large-scale multi-mission space campaign design by approximate dynamic programming

Hao Chen, Arthur Lapin, Chao Lei, Koki Ho, Takaya Ukai

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Scopus citations

    Abstract

    Architecture design and logistics mission planning are two important components of space campaign design. Given available architecture designs, multi-mission logistics mission planning problem typically can be solved for each mission independently. However, design of a multi-mission campaign considering the interactions among the missions is essential for optimal vehicle or other infrastructure designs; this campaign-level space mission design optimization problem over a long time horizon can become computationally prohibitive due to the curse of dimensionality. This paper proposed a lookahead-policy-based approximate dynamic programming (ADP) algorithm to design architectures effectively. It resolves the curse of dimensionality by considering the performance of architectures in the first few missions optimally and further future missions approximately. A case study of lunar exploration campaign design demonstrates the effectiveness of the proposed ADP algorithm. Results show that the ADP algorithm can provide a fast estimation of architecture designs. The solution approximates well traditional all-at-once mission planning optimization framework. Moreover, the proposed ADP algorithm is more scalable and flexible to balance the design fidelity and computational efficiency.

    Original languageEnglish
    Title of host publication2018 AIAA SPACE and Astronautics Forum and Exposition
    DOIs
    StatePublished - 2018
    EventAIAA Space and Astronautics Forum and Exposition, 2018 - Orlando, United States
    Duration: 17 Sep 201819 Sep 2018

    Publication series

    Name2018 AIAA SPACE and Astronautics Forum and Exposition

    Conference

    ConferenceAIAA Space and Astronautics Forum and Exposition, 2018
    Country/TerritoryUnited States
    CityOrlando
    Period17/09/1819/09/18

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