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Keywords
The optical properties of the thin metalized polymer films that are projected for solar sails are assumed to be affected by the erosive effects of the space environment. Their degradation behavior in the real space environment, however, is to a considerable degree indefinite, because initial ground test results are controversial and relevant inspace tests have not been made so far. The standard optical solar sail models that are currently used for trajectory design do not take optical degradation into account, hence its potential effects on trajectory design have not been investigated so far. Nevertheless, optical degradation is important for high-fidelity solar sail mission design, because it decreases both the magnitude of the solar radiation pressure force acting on the sail and also the sail control authority. Therefore, we propose a simple parametric optical solar sail degradation model that describes the variation of the sail film’s optical coefficients with time, depending on the sail film’s environmental history, i.e., the radiation dose. The primary intention of our model is not to describe the exact behavior of specific film-coating combinations in the real space environment, but to provide a more general parametric framework for describing the general optical degradation behavior of solar sails. Using our model, the effects of different optical degradation behaviors on trajectory design are investigated for various exemplary missions.
Searching optimal interplanetary trajectories for low-thrust spacecraft is usually a difficult and time-consuming task that involves much experience and expert knowledge in astrodynamics and optimal control theory. This is because the convergence behavior of traditional local optimizers, which are based on numerical optimal control methods, depends on an adequate initial guess, which is often hard to find, especially for very-low-thrust trajectories that necessitate many revolutions around the sun. The obtained solutions are typically close to the initial guess that is rarely close to the (unknown) global optimum. Within this paper, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning. Inspired by natural archetypes, a smart global method for low-thrust trajectory optimization is proposed that fuses artificial neural networks and evolutionary algorithms into so-called evolutionary neurocontrollers. This novel method runs without an initial guess and does not require the attendance of an expert in astrodynamics and optimal control theory. This paper details how evolutionary neurocontrol works and how it could be implemented. The performance of the method is assessed for three different interplanetary missions with a thrust to mass ratio <0.15mN/kg (solar sail and nuclear electric).