Multi-objective robust trajectory optimization of multi asteroid fly-by under epistemic uncertainty

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Abstract

Methods are proposed and compared to generate robust optimal trajectories subject to epistemic uncertainty, meaning uncertainties that derive from a lack of knowledge on system's and launcher's parameters.
This type of uncertainty is typical of the early stage of the design process when multiple options need to be evaluated and only a partial knowledge of each of them is available.
The uncertainty is modelled using probability boxes (p-boxes) and lower expectation. The p-box is a family of distributions that is known to contain the real probability distribution, and the lower expectation is the minimum expectation that can be obtained with distributions within that family. We test multiple methods for efficiently estimating this quantity.
These lower expectations are optimised using a Multi-Objective solver MACS (Multi Agent Collaborative Search), and with surrogate models to speed-up the optimization.
Furthermore, novel dimensionality reduction methods are employed, based on control mapping, as well as a method, threshold mapping, that improves the quality of the optimization by focusing the search on target sets that produce non-trivial values of the lower expectation.
Original languageEnglish
Title of host publicationAdvances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications
Subtitle of host publicationProceedings of the 2020 UQOP International Conference
EditorsMassimiliano Vasile, Domenico Quagliarella
Place of PublicationCham, Switzerland
PublisherSpringer
Pages209-230
Number of pages22
ISBN (Electronic)9783030805425
ISBN (Print)9783030805418
DOIs
Publication statusPublished - 16 Jun 2021
EventInternational Conference on Uncertainty Quantification & Optimisation - Online
Duration: 16 Nov 202019 Nov 2020
http://utopiae.eu/uqop-2020/

Conference

ConferenceInternational Conference on Uncertainty Quantification & Optimisation
Period16/11/2019/11/20
Internet address

Keywords

  • robust optimization
  • lower expectation
  • epistemic uncertainty
  • multi-objective optimization

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