An adaptive homotopy controller for low-thrust rendezvous uses MTF-augmented Kalman filter confidence to modulate control smoothness, reducing terminal miss distances by two orders of magnitude under degraded measurements.
Deep Networks as Approximators of Optimal Low -Thrust and Multi-Impulse Cost in Multitarget Missions
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Analytical gradients and Hessian for low-thrust rendezvous Δv enable efficient nonlinear programming of multi-asteroid trajectories.
Neural surrogates trained with scaling laws and self-similar transformations accurately approximate low-thrust trajectory costs and reachability while generalizing across orbital parameters.
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