Low-Thrust Trajectory Design Using Closed-Loop Feedback-Driven Control Laws and State-Dependent Parameters
2020
Low-thrust many-revolution trajectory design and orbit transfers are becoming increasingly
important with the development of high specific impulse, low-thrust engines. Closed-loop
feedback-driven (CLFD) control laws can be used to solve these trajectory design problems
with minimal computational cost and offer potential for autonomous guidance. However,
they have user-defined parameters which limit their optimality. In this work, an actor-critic
reinforcement learning framework is proposed to make the parameters of the Lyapunov-based
Q-law state-dependent, ensuring the controller can adapt as the dynamics evolve during a
transfer. The proposed framework should be independent of the particular CLFD control
law and provides improved solutions for mission analysis. There is also potential for future
on-board autonomous use, as trajectories are closed-form and can be generated without an
initial guess. The current results focus on GTO-GEO transfers in Keplerian dynamics and
later with eclipse and J2 effects. Both time-optimal and mass-optimal transfers are presented,
and the stability to uncertainties in orbit determination are discussed. The task of handling
orbit perturbations is left to future work.
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