Evidence-based robust optimization of pulsed laser orbital debris removal under epistemic uncertainty

Liqiang Hou, Massimiliano Vasile, Zhaohui Hou

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

An evidence-based robust optimization method for pulsed laser orbital debris removal (LODR) is presented. Epistemic type uncertainties due to limited knowledge are considered. The objective of the design optimization is set to minimize the debris lifetime while at the same time maximizing the corresponding belief value. The Dempster–Shafer theory of evidence (DST), which merges interval-based and probabilistic uncertainty modeling, is used to model and compute the uncertainty impacts. A Kriging based surrogate is used to reduce the cost due to the expensive numerical life prediction model. Effectiveness of the proposed method is illustrated by a set of benchmark problems. Based on the method, a numerical simulation of the removal of Iridium 33 with pulsed lasers is presented, and the most robust solutions with minimum lifetime under uncertainty are identified using the proposed method.

Original languageEnglish
Title of host publicationModeling and Optimization in Space Engineering
Subtitle of host publicationSpringer Optimization and its Applications
Place of PublicationZurich
PublisherSpringer International Publishing AG
Pages169-190
Number of pages22
ISBN (Print)9783030105006
DOIs
Publication statusPublished - 11 May 2019

Publication series

NameSpringer Optimization and Its Applications
Volume144
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

    Fingerprint

Keywords

  • laser orbital debris removal (LODR)
  • uncertainty modelling
  • epistemic uncertainty
  • astronautics
  • space debris

Cite this

Hou, L., Vasile, M., & Hou, Z. (2019). Evidence-based robust optimization of pulsed laser orbital debris removal under epistemic uncertainty. In Modeling and Optimization in Space Engineering: Springer Optimization and its Applications (pp. 169-190). (Springer Optimization and Its Applications; Vol. 144). Zurich: Springer International Publishing AG. https://doi.org/10.1007/978-3-030-10501-3_7