Genetic algorithms applied to the solution of hybrid optimal control problems in astrodynamics

  • Wall B
  • Conway B
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Many space mission planning problems may be formulated as hybrid optimal control problems, i.e. problems that include both
continuous-valued variables and categorical (binary) variables. There may be thousands to millions of possible solutions;
a current practice is to pre-prune the categorical state space to limit the number of possible missions to a number that may
be evaluated via total enumeration. Of course this risks pruning away the optimal solution. The method developed here avoids
the need for pre-pruning by incorporating a new solution approach using nested genetic algorithms; an outer-loop genetic algorithm
that optimizes the categorical variable sequence and an inner-loop genetic algorithm that can use either a shape-based approximation
or a Lambert problem solver to quickly locate near-optimal solutions and return the cost to the outer-loop genetic algorithm.
This solution technique is tested on three asteroid tour missions of increasing complexity and is shown to yield near-optimal,
and possibly optimal, missions in many fewer evaluations than total enumeration would require.

Author-supplied keywords

  • Bilevel programming problem (BLPP)
  • Genetic algorithm
  • Global trajectory optimization competition (GTOC)
  • Hybrid optimal control
  • Spacecraft trajectory optimization

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  • Bradley J. Wall

  • Bruce A. Conway

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