The paper proposes the application of multiobjective optimization to cope with the complex problem of fruitfully visiting the search space related to the interplanetary trajectory design, to get a space probe to its eventual target planet while minimizing the propellant mass and cleverly exploiting the celestial mechanics. Different control techniques are applied, those typical within the framework of space trajectory control: impulsive high thrust, continuous low thrust propulsion and the so-called Gravity-Assist (GA) maneuvers. The optimization is focused in detecting the best control sequence to minimize the propellant mass and the transfer time, being consistent with several physical and technological constraints. Because of wide dimension of the search domain, a dedicated optimization has been implemented to better guide the optimizer search. Results here offered show the fitness of the Particle Swarm Optimization to efficiently identifying preliminary optimal solutions to be further locally refined.

MOPSO Technique Assessment to Cope with First Guess Generation for Interplanetary Trajectories Differently Controlled

LAVAGNA, MICHÈLE;
2007

Abstract

The paper proposes the application of multiobjective optimization to cope with the complex problem of fruitfully visiting the search space related to the interplanetary trajectory design, to get a space probe to its eventual target planet while minimizing the propellant mass and cleverly exploiting the celestial mechanics. Different control techniques are applied, those typical within the framework of space trajectory control: impulsive high thrust, continuous low thrust propulsion and the so-called Gravity-Assist (GA) maneuvers. The optimization is focused in detecting the best control sequence to minimize the propellant mass and the transfer time, being consistent with several physical and technological constraints. Because of wide dimension of the search domain, a dedicated optimization has been implemented to better guide the optimizer search. Results here offered show the fitness of the Particle Swarm Optimization to efficiently identifying preliminary optimal solutions to be further locally refined.
58th International Astronautical Congress 2007
978-160560150-2
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/537349
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