Evolutionary Neurocontrol: A Novel Method for Low-Thrust Gravity-Assist Trajectory Optimization

Abstract

The combination of low-thrust propulsion and gravity assists to enhance deep-space missions has proven to be a remarkable task. In this paper, we present a novel method that is based on evolutionary neurocontrollers. The main advantage in the use of a neurocontroller is the generation of a control law with a limited number of decision variables. On the other hand, the evolutionary algorithm allows one to look for globally optimal solutions more efficiently than with a systematic search. In addition, a steepest-ascent algorithm is introduced that acts as a navigator during the planetary encounter, providing the neurocontroller with the optimal insertion parameters. Results are presented for a Mercury rendezvous with a Venus gravity assist and for a Pluto flyby with a Jupiter gravity assist.