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Abstract:
Initial experiments described here are directed toward using
reinforcement learning (RL) to develop an automatic recovery system
(ARS) for high-agility aircraft. An ARS is an outer-loop flight
control system designed to bring an aircraft from a range of
initial states to straight, level, and non-inverted flight in
minimum time and while satisfying given constraints. Here we report
results for a simple version of the problem involving only
single-axis (pitch) simulated recoveries. Through simulated control
experience using a medium-fidelity aircraft simulation, the RL
system approximated an optimal policy for longitudinal-stick inputs
to produce minimum-time transitions to straight and level flight in
unconstrained cases, while meeting a pilot-station acceleration
constraint.
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