Optimization of Interplanetary Rendezvous Trajectories for Solar Sailcraft Using a Neurocontroller

As for all low-thrust spacecraft, finding optimal solar sailcraft trajectories is a dicult and time-consuming task that involves a lot of experience and expert knowledge, since the convergence behavior of optimizers that are based on numerical optimal control methods depends strongly on an adequate initial guess, which is often hard to find. Even if the optimizer converges to an ”optimal trajectory”, this trajectory is typically close to the initial guess that is rarely close to the global optimum. This paper demonstrates, that artificial neural networks in combination with evolutionary algorithms can be applied successfully for optimal solar sailcraft steering. Since these evolutionary neurocontrollers explore the trajectory search space more exhaustively than a human expert can do by using traditional optimal control methods, they are able to find steering strategies that generate better trajectories, which are closer to the global optimum. Results are presented for a Near Earth Asteroid rendezvous mission and for a Mercury rendezvous mission.