Robust design of a reentry unmanned space vehicle by multifidelity evolution control

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimization technique is proposed that couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process and the hybrid algorithm searches for geometries that a) minimize the mean value of the maximum heat flux, b) maximize the mean value of the maximum achievable distance, and c) minimize the variance of the maximum heat flux. The evolutionary part handles the system design parameters of the vehicle and the uncertain functions, while the direct transcription method generates optimal control profiles for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are used to approximate the aerodynamic forces required by the direct transcription method. The artificial neural networks are trained and updated by means of a multi-fidelity, evolution control approach.
LanguageEnglish
Pages1284-1295
Number of pages12
JournalAIAA Journal
Volume51
Issue number6
DOIs
Publication statusPublished - Jun 2013

Fingerprint

Reentry
Transcription
Heat flux
Aerodynamics
Neural networks
Systems analysis
Trajectories
Geometry

Keywords

  • robust design
  • multifidelity
  • surrogate models
  • re-entry vehicles
  • evolutionary control
  • waverider aerodynamics
  • 2-stage-to-orbit missions

Cite this

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Robust design of a reentry unmanned space vehicle by multifidelity evolution control. / Minisci, Edmondo; Vasile, Massimiliano.

In: AIAA Journal, Vol. 51, No. 6, 06.2013, p. 1284-1295.

Research output: Contribution to journalArticle

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