The particle swarm optimization technique is a population-based stochasticmethod developed in recent years and successfully applied in several fields ofresearch. It represents a very intuitive (and easy to program) methodology forglobal optimization, inspired by the behavior of bird flocks while searching forfood. The particle swarm optimization technique attempts to take advantage ofthe mechanism of information sharing that affects the overall behavior of aswarm, with the intent of determining the optimal values of the unknown parametersof the problem under consideration. This research applies the techniqueto determining optimal continuous-thrust rendezvous trajectories in a rotatingEuler-Hill frame. Hamiltonian methods are employed to translate the relatedoptimal control problems into parameter optimization problems. Thus theparameters sought by the swarming algorithm are primarily the initial values ofthe costates and the final time. The algorithm at hand is extremely easy to program.Nevertheless, it proves to be effective, reliable, and numerically accuratein solving the rendezvous optimization problems considered in this work.
展开▼
