Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning

TL;DR

This paper presents a reinforcement meta learning-based adaptive guidance system for hypersonic gliders, capable of handling diverse off-nominal conditions and maintaining high accuracy during the approach phase.

Contribution

It introduces a novel reinforcement meta learning approach that optimizes guidance for hypersonic vehicles across various unpredictable flight scenarios.

Findings

Achieves high-accuracy target approach under diverse conditions

Outperforms linear quadratic regulator in trajectory tracking

Maintains safety constraints like heating and load limits

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.