Adaptive Approach Phase Guidance for a Hypersonic Glider via
Reinforcement Meta Learning
- URL: http://arxiv.org/abs/2107.14764v1
- Date: Fri, 30 Jul 2021 17:14:52 GMT
- Title: Adaptive Approach Phase Guidance for a Hypersonic Glider via
Reinforcement Meta Learning
- Authors: Brian Gaudet, Kris Drozd, Ryan Meltzer, Roberto Furfaro
- Abstract summary: Adaptability is achieved by optimizing over a range of off-nominal flight conditions.
System maps observations directly to commanded bank angle and angle of attack rates.
Minimizing the tracking error keeps the curved space line of sight to the target location aligned with the vehicle's velocity vector.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We use Reinforcement Meta Learning to optimize an adaptive guidance system
suitable for the approach phase of a gliding hypersonic vehicle. Adaptability
is achieved by optimizing over a range of off-nominal flight conditions
including perturbation of aerodynamic coefficient parameters, actuator failure
scenarios, and sensor noise. The system maps observations directly to commanded
bank angle and angle of attack rates. These observations include a velocity
field tracking error formulated using parallel navigation, but adapted to work
over long trajectories where the Earth's curvature must be taken into account.
Minimizing the tracking error keeps the curved space line of sight to the
target location aligned with the vehicle's velocity vector. The optimized
guidance system will then induce trajectories that bring the vehicle to the
target location with a high degree of accuracy at the designated terminal
speed, while satisfying heating rate, load, and dynamic pressure constraints.
We demonstrate the adaptability of the guidance system by testing over flight
conditions that were not experienced during optimization. The guidance system's
performance is then compared to that of a linear quadratic regulator tracking
an optimal trajectory.
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