# Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2406.05586 · HTML: https://mlsanity.com/paper/2406.05586/

## Key facts

- Cited by 1 papers 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.

## Placed in

- Deep reinforcement learning methods / [Safe and constrained RL](https://mlsanity.com/deeprl/safe-and-constrained-rl/)

## Builds on (most cited first, 2 of 2)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)

## Built on by (most cited first, 1 of 1)

- [Reciprocal Trust and Distrust in Artificial Intelligence Systems: The Hard Problem of Regulation](https://mlsanity.com/paper/2604.05826/) (2026)

## 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.