Spaceplane trajectory optimisation with evolutionary-based initialisation

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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.
Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Place of PublicationPiscataway, NJ.
PublisherIEEE
Number of pages8
ISBN (Electronic)9781509042401
ISBN (Print)9781509042418
DOIs
Publication statusPublished - 9 Dec 2016
EventIEEE Symposium Series on Computational Intelligence - Royal Olympic Hotel, Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Conference

ConferenceIEEE Symposium Series on Computational Intelligence
CountryGreece
CityAthens
Period6/12/169/12/16

Keywords

  • trajectory optimisation
  • space access
  • single stage to orbit
  • evolutionary algorithm
  • adaptive inflationary differential evolution algorithm
  • deterministic local optimisation algorithm
  • spaceplane

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