Multi-Objective Optimisation under Uncertainty with Unscented Temporal Finite Elements

Lorenzo A. Ricciardi, Christie Alisa Maddock and Massimiliano Vasile
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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

Mathematics, 2021, vol. 9, issue 23, 1-22

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.

Keywords: optimal control; multi-objective optimisation; robust design; trajectory optimisation; uncertainty quantification; unscented transformation; spaceplanes; space systems; launchers (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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