design space pruning heuristics and global optimization method for ...

the environment. For an evolutionary neurocontroller, an evolutionary algorithm is used
to find the neurocontroller’s optimal network function.
Dachwald’s work applies an evolutionary neurocontroller to solar sail trajectories,
which have very low thrust magnitudes, thereby exhibiting solutions with many
revolutions around the Sun. Furthermore, the objective function is generally to minimize
the time of flight since there is no propulsion required for a solar sail. More recently,
however, Dachwald applied his method to solar electric propulsion (SEP) spacecraft as
well62. In his formulation, a trajectory is the result of a spacecraft steering strategy that
controls the spacecraft’s thrust vector according to the current state of the spacecraft
relative to the target. An artificial neural network is then used to implement the
spacecraft steering strategy, with the evolutionary algorithm used to optimize the
neurocontroller parameters. Figure 6 illustrates how such a formulation works for the
SEP trajectory. The neural network pictured below illustrates how the inputs for a SEP
trajectory are mapped to outputs, as per Dachwald’s formulation. Here, the inputs
represent the difference in the spacecraft’s state and its target at any point along the
trajectory. The output then corresponds to the control parameters that will result in the
spacecraft meeting its target constraints at the specified final time.
In his example case, Dachwald utilized the evolutionary neurocontroller to
optimize the launch date in addition to the spacecraft steering strategy. He did not,
however, consider problems with multiple legs or encounter bodies. The evolutionary
neurocontroller was applied to a Mercury rendezvous and a near-Earth asteroid
rendezvous, and compared to similar problems in the literature. Dachwald’s method was
able to locate solutions better than those presented in the literature, due to its ability to
search a large portion of the design space.
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