Title:
Costates Feedback Control for Mass-Optimal Low-Thrust Transfers
Costates Feedback Control for Mass-Optimal Low-Thrust Transfers
Author(s)
Shimane, Yuri
Izzo, Dario
Ho, Koki
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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Date Issued
2023-08
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Text
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Paper
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