Optimization of Interplanetary Solar Sailcraft Trajectories Using Evolutionary Neurocontrol

Abstract

Solar sailcraft provide a wide range of opportunities for high-energy low-cost missions. As for all low-thrust spacecraft, finding optimal trajectories is a difficult and time-consuming task that involves a lot of experience and expert knowledge because 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. Artificial neural networks in combination with evolutionary algorithms can be applied successfully for optimal solar sail steering. Because 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 sail steering strategies that generate better trajectories that are closer to the global optimum. Results are presented fo ra near Earth asteroid rendezvous mission, a Mercury rendezvous mission, and a Pluto flyby mission, which are then compared with previous results found in the literature.