Evolutionary Neurocontrol as 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 formidable task. While trajectories generated by methods based on optimal control theory are typically close to the required initial guess, recently investigated global evolutionary programming techniques often necessitate the successive use of different methods. In this paper, we present a new method that is based on evolutionary neurocontrollers. The advantage lies in its ability to explore the solution space autonomously to find optimal trajectories, without requiring an initial guess and the permanent attendance of an expert. 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. They show very good agreement with the reference trajectories, in particular virtually no further refinement of the solution is required.