<h2>AAS 99-123</h2>
<h2>Optimal Station Keeping Strategies via Parallel Genetic Algorithms                                                                               </h2>
<h4>J. Smith*, R. Proulx*, P. Cefola*, J. Draim**                                                                                                                                       </h4>
The Charles Stark Draper Lab, Cambridge, MA, ** ELLIPSO, Washington, DC                                                                                                   
<h2> Abstract </h2>
In an effort to overcome the limitations of more traditional methods this paper investigates the use of genetic algorithms in generating optimal station-keeping strategies.  The orbit of an EllipsoTM Borealis satellite is constrained and the minimum-fuel optimal burn strategy is developed such that the orbit is maintained within the specified constraints.  The resulting fuel costs are shown to be lower than costs estimated via previous methods, specifically previous primer vector approaches.  Operational and computational limitations of this approach are also described.