Deep Reinforcement Learning based Control Design for Aircraft Recovery from Loss-of-Control Scenario

TL;DR

This paper develops a deep reinforcement learning controller for aircraft spin recovery, demonstrating its ability to stabilize the aircraft in complex, nonlinear scenarios more effectively than traditional methods.

Contribution

It introduces a novel RL-based control approach for aircraft loss-of-control recovery, trained on a high-fidelity aircraft model with nonlinear dynamics.

Findings

RL policy stabilizes aircraft in spin scenarios

Policy generalizes to unseen initial conditions

Performance exceeds traditional sliding mode controllers

Abstract

Loss-of-control (LOC) remains a leading cause of fixed-wing aircraft accidents, especially in post-stall and flat-spin regimes where conventional gain-scheduled or logic-based recovery laws may fail. This study formulates spin-recovery as a continuous-state, continuous-action Markov Decision Process and trains a Proximal Policy Optimization (PPO) agent on a high-fidelity six-degree-of-freedom F-18/HARV model that includes nonlinear aerodynamics, actuator saturation and rate coupling. A two-phase potential-based reward structure first penalizes large angular rates and then enforces trimmed flight. After 6,000 simulated episodes, the policy generalities to unseen upset initializations. Results show that the learned policy successfully arrests the angular rates and stabilizes the angle of attack. The controller performance is observed to be satisfactory for recovery from spin condition which was compared with a state-of-the-art sliding mode controller. The findings demonstrate that deep reinforcement learning can deliver interpretable, dynamically feasible manoeuvres for real-time loss of control mitigation and provide a pathway for flight-critical RL deployment.