Title:
Costates Feedback Control for Mass-Optimal Low-Thrust Transfers

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Shimane, Yuri
Izzo, Dario
Ho, Koki
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Abstract
Designing efficient low-thrust trajectories involves solving optimal control problems, which can be computationally intensive. One promising approach to tackle this challenge is to use a neural network (NN) as a scheme for obtaining the control input that guides the spacecraft to its targeted orbit. This work explores the use of a NN for learning the costates from the current and targeted states, which can be used together with Pontryagin's maximum principle and optimal control theory to derive the control to provide a feedback loop for controlling the spacecraft. In effect, this is a policy approximation scheme, even though it does not explicitly have the controls as its output. The proposed method is applied to a set of orbits departing from near-ecliptic near-Earth object targeting the Earth's orbit for a mass optimal orbit transfer.
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2023-08
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