Ian Carnelli, Bernd Dachwald, Massimiliano Vasile, Wolfgang Seboldt, Amalia Ercoli Finzi
Low-Thrust Gravity Assist Trajectory Optimization Using Evolutionary Neurocontrollers
In: Advances in the Astronautical Sciences, Vol. 123: Astrodynamics 2005 Part III (Proceedings of the AAS/AIAA Astrodynamics Conference 2005, Lake Tahoe (CA), USA) Univelt, San Diego, pp. 1911-1928 (AAS-05-374)


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 gravity assist parameters. First results are presented for a Mercury rendezvous with a Venus gravity assist and for a Pluto flyby with a Jupiter gravity assist. They show excellent agreement with the reference trajectories, in particular virtually no further refinement of the solution is required.