Abstract
A hybrid trajectory optimization procedure for a class of solar-electric-propulsion, gravity-assist, outer-planet missions is presented. The parameter space of a target mission is often nonconvex and a calculus-of-variations-based optimization algorithm suffers difficulties efficiently exploring this space. A hybrid procedure using a genetic algorithm to drive a calculus-of-variations program is developed to automate searching over a reduced parameter space. Employing the hybrid procedure, the delivered mass profiles of a Uranus and Pluto mission are generated more quickly than by using the calculus-of-variations optimization algorithm alone.
Original language | English (US) |
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Pages (from-to) | 121-129 |
Number of pages | 9 |
Journal | Journal of Spacecraft and Rockets |
Volume | 43 |
Issue number | 1 |
DOIs | |
State | Published - 2006 |
ASJC Scopus subject areas
- Aerospace Engineering
- Space and Planetary Science