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90 Bernd Dachwaldreference launch date, the constellation of Earth and Mercury is most similar to that of the optimalorbit transfer solution for a launch on 27 Mar 03. Therefore, InTrance was run five times,being allowed to choose the launch date from the interval [26 Mar 03, 31 Mar 03]. The maximumtransfer time was set again to T max = 600 days (with τ = 600). The small variation ofthe solutions shown in Fig. 2 (4 days) gives again evidence for a good convergence behavior.Taking 502 days to rendezvous Mercury, the best InTrance-trajectory is now 163 days fasterthan the reference trajectory, the accuracy again being well better than required.4. Summary and ConclusionsWithin the work described here, low-thrust trajectory optimization was attacked from theperspective of machine learning. A novel global method for spacecraft trajectory optimization,termed InTrance, was proposed. It fuses artificial neural networks and evolutionaryalgorithms into evolutionary neurocontrollers. The re-calculation of an exemplary Mercuryrendezvous mission with a solar sail revealed that a reference trajectory, which was generatedby a trajectory optimization expert using a local trajectory optimization method, is quite farfrom the global optimum. Using InTrance, the transfer time could be reduced considerably.InTrance runs without an initial guess and does not require the attendance of an expert inastrodynamics and optimal control theory. Being problem-independent, its application fieldmay be extended to a variety of other optimal control problems.References[1] B. Dachwald. Minimum transfer times for nonperfectly reflecting solar sailcraft. Journal of Spacecraft andRockets, 41(4):693–695.[2] B. Dachwald. Optimal solar sail trajectories for missions to the outer solar system. Journal of Guidance,Control, and Dynamics. in press.[3] B. Dachwald. Optimization of interplanetary solar sailcraft trajectories using evolutionary neurocontrol.Journal of Guidance, Control, and Dynamics, 27(1):66–72.[4] B. Dachwald. Low-Thrust Trajectory Optimization and Interplanetary Mission Analysis Using Evolutionary Neurocontrol.Doctoral thesis, Universität der Bundeswehr München; Fakultät für Luft- und Raumfahrttechnik,2004.[5] B. Dachwald. Optimization of very-low-thrust trajectories using evolutionary neurocontrol. Acta Astronautica,57(2-8):175–185, 2005.[6] B. Dachwald and W. Seboldt. Multiple near-earth asteroid rendezvous and sample return using first generationsolar sailcraft. Acta Astronautica, 2005. in press.[7] D. C. Dracopoulos. Evolutionary Learning Algorithms for Neural Adaptive Control. Perspectives in NeuralComputing. Springer, Berlin, Heidelberg, New York, 1997.[8] M. Leipold. Solar Sail Mission Design. Doctoral thesis, Lehrstuhl für Flugmechanik und Flugregelung; TechnischeUniversität München, 1999. DLR-FB-2000-22.[9] M. Leipold, W. Seboldt, S. Lingner, E. Borg, A. Herrmann, A. Pabsch, O. Wagner, and J. Brückner. Mercurysun-synchronous polar orbiter with a solar sail. Acta Astronautica, 39(1-4):143–151, 1996.[10] W. Seboldt and B. Dachwald. Solar sailcraft of the first generation: Technology development. Bremen,Germany, September/October 2003. 54 th International Astronautical Congress. IAC-03-S.6.03.[11] R. S. Sutton and A. G. Barto. Reinforcement Learning. MIT Press, Cambridge, London, 1998.