Multi-objective optimisation under uncertainty with unscented temporal finite elements

Lorenzo Angelo Ricciardi; Christie Maddock; Massimiliano Vasile

doi:10.3390/math9233010

Multi-objective optimisation under uncertainty with unscented temporal finite elements

Lorenzo Angelo Ricciardi, Christie Maddock, Massimiliano Vasile

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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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.

Original language	English
Title of host publication	Evolutionary Algorithms in Engineering Design Optimization
Editors	David Greiner, Antonio Gaspar-Cunha, Daniel Hernández-Sosa, Edmondo Minisci, Aleš Zamuda
Place of Publication	Basel
Publisher	MDPI Multidisciplinary Digital Publishing Institute
Pages	187-208
Number of pages	22
ISBN (Electronic)	9783036527154
ISBN (Print)	9783036527147
DOIs	https://doi.org/10.3390/math9233010
Publication status	Published - 1 Mar 2022

Keywords

optimal control
multi-objective optimisation
robust design
trajectory optimisation
uncertainty quantification
unscented transformation
spaceplanes
space systems
launchers

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3390/math9233010Licence: CC BY 4.0

Ricciardi-etal-EAEDO-2022-Multi-objective-optimisation-under-uncertainty-with-unscented-temporal-finite-elementsFinal published version, 2.55 MBLicence: CC BY 4.0

1 Finished

ESA NPI on Multi-Objective Hybrid Optimal Control Problems
Vasile, M. (Principal Investigator)
European Space Agency ESA
1/01/15 → 2/03/18
Project: Research - Studentship

1 Special issue

Multi-objective optimisation under uncertainty with unscented temporal finite elements
Ricciardi, L. A., Maddock, C. A. & Vasile, M., 24 Nov 2021, In: Mathematics. 9, 23, 22 p., 3010.
Research output: Contribution to journal › Special issue › peer-review

Open Access
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2 Citations (Scopus)

45 Downloads (Pure)

Cite this

Ricciardi, L. A., Maddock, C., & Vasile, M. (2022). Multi-objective optimisation under uncertainty with unscented temporal finite elements. In D. Greiner, A. Gaspar-Cunha, D. Hernández-Sosa, E. Minisci, & A. Zamuda (Eds.), Evolutionary Algorithms in Engineering Design Optimization (pp. 187-208). MDPI Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/math9233010

Ricciardi, Lorenzo Angelo ; Maddock, Christie ; Vasile, Massimiliano. / Multi-objective optimisation under uncertainty with unscented temporal finite elements. Evolutionary Algorithms in Engineering Design Optimization. editor / David Greiner ; Antonio Gaspar-Cunha ; Daniel Hernández-Sosa ; Edmondo Minisci ; Aleš Zamuda. Basel : MDPI Multidisciplinary Digital Publishing Institute, 2022. pp. 187-208

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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.",

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author = "Ricciardi, {Lorenzo Angelo} and Christie Maddock and Massimiliano Vasile",

note = "The book in which this chapter has been published is a reprint of articles from the Special Issue published online in the open access journal Mathematics (ISSN 2227-7390) (available at: https://www.mdpi.com/journal/mathematics/special issues/Evolutionary Algorithms Engineering Design Optimization)",

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Ricciardi, LA, Maddock, C & Vasile, M 2022, Multi-objective optimisation under uncertainty with unscented temporal finite elements. in D Greiner, A Gaspar-Cunha, D Hernández-Sosa, E Minisci & A Zamuda (eds), Evolutionary Algorithms in Engineering Design Optimization. MDPI Multidisciplinary Digital Publishing Institute, Basel, pp. 187-208. https://doi.org/10.3390/math9233010

Multi-objective optimisation under uncertainty with unscented temporal finite elements. / Ricciardi, Lorenzo Angelo; Maddock, Christie ; Vasile, Massimiliano.
Evolutionary Algorithms in Engineering Design Optimization. ed. / David Greiner; Antonio Gaspar-Cunha; Daniel Hernández-Sosa; Edmondo Minisci; Aleš Zamuda. Basel: MDPI Multidisciplinary Digital Publishing Institute, 2022. p. 187-208.

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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Ricciardi LA, Maddock C , Vasile M. Multi-objective optimisation under uncertainty with unscented temporal finite elements. In Greiner D, Gaspar-Cunha A, Hernández-Sosa D, Minisci E, Zamuda A, editors, Evolutionary Algorithms in Engineering Design Optimization. Basel: MDPI Multidisciplinary Digital Publishing Institute. 2022. p. 187-208 Epub 2021 Nov 24. doi: 10.3390/math9233010

Multi-objective optimisation under uncertainty with unscented temporal finite elements

Abstract

Keywords

UN SDGs

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Projects

ESA NPI on Multi-Objective Hybrid Optimal Control Problems

Research output

Multi-objective optimisation under uncertainty with unscented temporal finite elements

Cite this