@inproceedings{Dachwald2005,
  author    = {Dachwald, Bernd},
  title     = {Global optimization of low-thrust space missions using evolutionary neurocontrol},
  series = {Proceedings of the international workshop on global optimization},
  booktitle = {Proceedings of the international workshop on global optimization},
  pages     = {85 -- 90},
  year      = {2005},
  abstract  = {Low-thrust space propulsion systems enable flexible high-energy deep space missions, but the design and optimization of the interplanetary transfer trajectory is usually difficult. It involves much experience and expert knowledge because the convergence behavior of traditional local trajectory optimization methods depends strongly on an adequate initial guess. Within this extended abstract, evolutionary neurocontrol, a method that fuses artificial neural networks and evolutionary algorithms, is proposed as a smart global method for low-thrust trajectory optimization. It does not require an initial guess. The implementation of evolutionary neurocontrol is detailed and its performance is shown for an exemplary mission.},
  language  = {en}
}
@article{Dachwald2005,
  author    = {Dachwald, Bernd},
  title     = {Optimization of very-low-thrust trajectories using evolutionary neurocontrol},
  series = {Acta Astronautica},
  volume    = {57},
  journal   = {Acta Astronautica},
  number    = {2-8},
  publisher = {Elsevier},
  address   = {Amsterdam [u.a.]},
  isbn      = {1879-2030},
  pages     = {175 -- 185},
  year      = {2005},
  abstract  = {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).},
  language  = {en}
}