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Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach

Akin Catak, Ege C. Altunkaya, Mustafa Demir, Emre Koyuncu, Ibrahim Ozkol

TL;DR

This work addresses safe flight envelope protection for the longitudinal axis under nonlinear aircraft dynamics by introducing a reinforcement-learning agent trained with a reward that penalizes envelope violations and tracking errors. The approach uses Deep Deterministic Policy Gradient (DDPG) within a nonlinear F-16 model integrated with an INDI control law, with the agent outputting restorative pitch-rate commands $q_{rest}$. Key contributions include a novel RL-based envelope protection method that reduces design burden and improves performance in coupled maneuvers, demonstrated across multiple scenarios and Monte Carlo tests. The results suggest RL-enabled envelope protection offers carefree flight capabilities with scalable applicability, paving the way for full-envelope protection including lateral dynamics.

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.

Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach

TL;DR

This work addresses safe flight envelope protection for the longitudinal axis under nonlinear aircraft dynamics by introducing a reinforcement-learning agent trained with a reward that penalizes envelope violations and tracking errors. The approach uses Deep Deterministic Policy Gradient (DDPG) within a nonlinear F-16 model integrated with an INDI control law, with the agent outputting restorative pitch-rate commands . Key contributions include a novel RL-based envelope protection method that reduces design burden and improves performance in coupled maneuvers, demonstrated across multiple scenarios and Monte Carlo tests. The results suggest RL-enabled envelope protection offers carefree flight capabilities with scalable applicability, paving the way for full-envelope protection including lateral dynamics.

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.
Paper Structure (16 sections, 20 equations, 7 figures, 1 table)

This paper contains 16 sections, 20 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. State-of-Art
  3. Problem Statement
  4. Contributions
  5. Preliminaries
  6. Nonlinear Flight Dynamics
  7. Equations of Motion
  8. Aerodynamics and Actuators
  9. Flight Control Laws
  10. Classical Flight Envelope Protection
  11. Angle of Attack and Load Factor Protection
  12. Reinforcement Learning
  13. Actor Critic - Deep Deterministic Policy Gradient
  14. Methodology and Reward Structure
  15. Results
  16. ...and 1 more sections

Figures (7)

  • Figure 1: The classical angle of attack and load factor protection scheme.
  • Figure 2: Actor-critic scheme.
  • Figure 3: The comparison of envelope protection algorithms: scenario-$\#1$.
  • Figure 4: The comparison of envelope protection algorithms: scenario-$\#2$.
  • Figure 5: The comparison of envelope protection algorithms: scenario-$\#3$.
  • ...and 2 more figures