Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach

TL;DR

This paper presents a reinforcement learning-based algorithm for flight envelope protection that automatically manages safety limits on aircraft variables, offering a scalable and less manual alternative to traditional methods.

Contribution

It introduces a novel RL approach for flight envelope protection that simplifies design and improves safety management compared to traditional manual tuning methods.

Findings

RL effectively enforces flight safety limits

The method reduces manual tuning requirements

Promising results in enhancing flight safety

Abstract

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.