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Multi-Objective Optimisation under Uncertainty with Unscented Temporal Finite Elements

Author

Listed:
  • Lorenzo A. Ricciardi

    (Aerospace Centre of Excellence, University of Strathclyde, Glasgow G1 1XJ, UK)

  • Christie Alisa Maddock

    (Aerospace Centre of Excellence, University of Strathclyde, Glasgow G1 1XJ, UK)

  • Massimiliano Vasile

    (Aerospace Centre of Excellence, University of Strathclyde, Glasgow G1 1XJ, UK)

Abstract

This paper presents a novel method for multi-objective optimisation under uncertainty developed to study a range of mission trade-offs, and the impact of uncertainties on the evaluation of launch system mission designs. A memetic multi-objective optimisation algorithm, named MODHOC, which combines the Direct Finite Elements in Time transcription method with Multi Agent Collaborative Search, is extended to account for model uncertainties. An Unscented Transformation is used to capture the first two statistical moments of the quantities of interest. A quantification model of the uncertainty was developed for the atmospheric model parameters. An optimisation under uncertainty was run for the design of descent trajectories for a spaceplane-based two-stage launch system.

Suggested Citation

  • Lorenzo A. Ricciardi & Christie Alisa Maddock & Massimiliano Vasile, 2021. "Multi-Objective Optimisation under Uncertainty with Unscented Temporal Finite Elements," Mathematics, MDPI, vol. 9(23), pages 1-22, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3010-:d:686603
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    References listed on IDEAS

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    1. Scott D. Chasalow & Richard J. Brand, 1995. "Generation of Simplex Lattice Points," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(4), pages 534-545, December.
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