mlsanityData to 14 Oct 2026

Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach

arXiv 2406.05586 ↗First posted 8 Jun 2024eess.SY cs.SYcited by 1

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

This paper introduces a flight envelope protection algorithm on a longitudinal axis that leverages reinforcement learning (RL). By considering limits on variables such as angle of attack, load factor, and pitch rate, the algorithm counteracts excessive pilot or control commands with restoring actions. Unlike traditional methods requiring manual tuning, RL facilitates the approximation of complex functions within the trained model, streamlining the design process. This study demonstrates the promising results of RL in enhancing flight envelope protection, offering a novel and easy-to-scale method for safety-ensured flight.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 16 references that are in the map. First posted on arXiv 8 Jun 2024.

Builds on

2 of 16 references.

References · 2

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 371

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

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