Low-Thrust Trajectory Optimization and Interplanetary Mission Analysis Using Evolutionary Neurocontrol
Deutscher Luft- und Raumfahrtkongress 2004, Dresden, Germany
The design and optimization of interplanetary transfer trajectories is one of the most important tasks during the analysis and design of a deep space mission. Due to their larger ΔV-capability, low-thrust propulsions systems can significantly enhance or even enable those missions. Searching low-thrust trajectories that are optimal with respect to transfer time or propellant consumption is usually a difficult and time-consuming task that involves much experience and expert knowledge, because 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 finally 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 paper, trajectory optimization is attacked from the perspective of artificial intelligence and machine learning, which is a perspective quite different from that of optimal control theory. Inspired by natural archetypes, a novel smart method for global low-thrust trajectory optimization is presented that fuses artificial neural networks and evolutionary algorithms to so-called evolutionary neurocontrollers. This paper outlines how evolutionary neurocontrol works and how it could be implemented. Using evolutionary neurocontrol, low-thrust trajectories are optimized without an initial guess and without the attendance of an expert in astrodynamics and optimal control theory. For an exemplary mission to a near-Earth asteroid, its performance for low-thrust trajectory optimization and interplanetary mission analysis is assessed. It is demonstrated that evolutionary neurocontrollers are able to find spacecraft steering strategies that generate better trajectories - closer to the global optimum - because they explore the search space more exhaustively than a human expert can do by using traditional optimal control methods. Finally, the use of evolutionary neurocontrol for the analysis of a piloted Mars mission using a spacecraft with a nuclear electric propulsion system is demonstrated within this paper.