TY - GEN
T1 - Optimization for large-scale multi-mission space campaign design by approximate dynamic programming
AU - Chen, Hao
AU - Lapin, Arthur
AU - Lei, Chao
AU - Ho, Koki
AU - Ukai, Takaya
N1 - Publisher Copyright:
© 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
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U2 - 10.2514/6.2018-5287
DO - 10.2514/6.2018-5287
M3 - Conference contribution
AN - SCOPUS:85056177067
SN - 9781624105753
T3 - 2018 AIAA SPACE and Astronautics Forum and Exposition
BT - 2018 AIAA SPACE and Astronautics Forum and Exposition
T2 - AIAA Space and Astronautics Forum and Exposition, 2018
Y2 - 17 September 2018 through 19 September 2018
ER -