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

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 mul...

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Bibliographic Details
Published in:Acta astronautica 2014-10, Vol.103, p.185-192
Main Author: Melton, Robert G.
Format: Article
Language:English
Subjects:
Algorithms
Bacteria
Cost function
Covariance matrix
Covariance matrix adaptation
Estimates
Optimal attitude control
Satellites
Searching
Stagnation
Time-optimal reorientation
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Summary: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. •Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES) was examined.•CMA-ES was successfully applied to the problem of time-optimal reorientation.•CMA-ES yields very good approximate solutions to problems with no path constraints.•Including path constraints, a 2-stage method (CMA-ES, pseudospectral) works well.
ISSN:0094-5765
1879-2030
DOI:10.1016/j.actaastro.2014.06.032