Low-Thrust Trajectory Optimization
and Interplanetary Mission Analysis Using Evlutionary Neurocontrol
Doctoral Thesis, University of the Federal Armed Forces Munich, Germany, 2004
Innovative solar system exploration missions require ever larger velocity
increments and thus ever more demanding propulsion capabilities. Using for those
high-energy missions the state-of-the-art technique of chemical propulsion in
combination with (eventually multiple) gravity assist maneuvers results in long,
complicated, and inflexible mission profiles. Low-thrust propulsions systems can
significantly enhance or even enable those high-energy missions, since they
utilize the propellant more efficiently - like electric propulsion systems - or
do not consume any propellant at all - like solar sails, that utilize solely the
freely available solar radiation pressure for propulsion. Consequently,
low-thrust propulsion systems permit significantly larger velocity increments
and/or larger payload ratios and/or smaller launch vehicles, while at the same
time allowing direct trajectories with reduced flight times, simpler mission
profiles, and extended launch windows.
One of the most important tasks during the feasibility analysis and the preliminary design of a deep space mission is the design and the optimization of the interplanetary transfer trajectory. Searching trajectories for low-thrust spacecraft, that are optimal with respect to transfer time or propellant consumption, is usually a difficult and time-consuming task that involves a lot of experience and expert knowledge, since the convergence behavior of traditional 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 (unknown) global optimum.
Within this work, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning, which is quite different from that of optimal control theory. Inspired by natural archetypes, a smart method for spacecraft trajectory optimization - that fuses artificial neural networks and evolutionary algorithms to evolutionary neurocontrollers - is developed. Before the novel method is employed for the trajectory optimization and mission analysis of some exemplary deep space missions, its convergence behavior is evaluated and the quality of the obtained solutions is assessed. It is demonstrated, by re-calculating trajectories for several existing low-thrust problems, that this novel method can be applied successfully for near-globally optimal spacecraft steering. Since 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 spacecraft steering strategies that generate better trajectories, which are closer to the global optimum. Using evolutionary neurocontrollers, low-thrust trajectories can be optimized without an initial guess and without the permanent attendance of an expert in astrodynamics and optimal control theory. Their field of application may be extended to a variety of optimal control problems.