Maximum-likelihood estimation optimizer for constrained, time-optimal satellite reorientation

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES) method provides a high-quality estimate of the control solution for an unconstrained satellite reorientation problem, and rapid, useful guesses needed for high-fidelity methods that can solve time-optimal reorientation problems with multiple path constraints. The CMA-ES algorithm offers two significant advantages over heuristic methods such as Particle Swarm or Bacteria Foraging Optimisation: it builds an approximation to the covariance matrix for the cost function, and uses that to determine a direction of maximum likelihood for the search, reducing the chance of stagnation; and it achieves second-order, quasi-Newton convergence behaviour.

Original languageEnglish (US)
Pages (from-to)185-192
Number of pages8
JournalActa Astronautica
Volume103
DOIs
StatePublished - Jan 1 2014

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Maximum likelihood estimation
Covariance matrix
Satellites
Heuristic methods
Evolutionary algorithms
Cost functions
Maximum likelihood
Bacteria

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

Cite this

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Maximum-likelihood estimation optimizer for constrained, time-optimal satellite reorientation. / Melton, Robert Graham.

In: Acta Astronautica, Vol. 103, 01.01.2014, p. 185-192.

Research output: Contribution to journalArticle

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