Spaceplane trajectory optimisation with evolutionary-based initialisation

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Maddock, Christie Alisa and Minisci, Edmondo; (2016) Spaceplane trajectory optimisation with evolutionary-based initialisation. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, GRC. ISBN 9781509042401 (https://doi.org/10.1109/SSCI.2016.7850109)

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Abstract

In this paper, an evolutionary-based initialisation method is proposed based on Adaptive Inflationary Differential Evolution algorithm, which is used in conjunction with a deterministic local optimisation algorithm to efficiently identify clusters of optimal solutions. The approach is applied to an ascent trajectory for a single stage to orbit spaceplane, employing a rocket-based combine cycle propulsion system. The problem is decomposed first into flight phases, based on user defined criteria such as a propulsion cycle change translating to different mathematical system models, and subsequently transcribed into a multi-shooting NLP problem. Examining the results based on 10 independent runs of the approach, it can be seen that in all cases the method converges to clusters of feasible solutions. In 40% of the cases, the AIDEA-based initialisation found a better solution compared to a heuristic approach using constant control for each phase with a single shooting transcription (representing an expert user). The problem was run using randomly generated control laws, only 2/20 cases converged, both times with a less optimal solution compared to the baseline heuristic approach and AIDEA.

ORCID iDs

Maddock, Christie Alisa ORCID logoORCID: https://orcid.org/0000-0003-1079-4863 and Minisci, Edmondo ORCID logoORCID: https://orcid.org/0000-0001-9951-8528;
  • Item type:Book Section
    ID code:59171
    Dates:
    Date
    Event
    9 December 2016
    Published
    26 September 2016
    Accepted
    Notes:© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics
    Technology > Motor vehicles. Aeronautics. Astronautics
    Department:Faculty of Engineering > Mechanical and Aerospace Engineering
    Depositing user: Pure Administrator
    Date deposited:20 Dec 2016 10:46
    Last modified:11 Nov 2024 15:08
    URI:https://strathprints.strath.ac.uk/id/eprint/59171
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