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Reinforcement learning for UAV flight controls: Evaluating continuous space reinforcement learning algorithms for

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  • 1School of Interdisciplinary Engineering and Sciences (SINES), National University of Science and Technology, Islamabad, Pakistan.

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Reinforcement learning (RL) enhances flight controls for fixed-wing unmanned aerial vehicles (UAVs). The Soft Actor-Critic (SAC) algorithm demonstrated superior stability and responsiveness compared to other RL methods and PID controllers.

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence in Aerospace
  • Autonomous Systems Engineering

Background:

  • Flight control systems are increasingly integrating reinforcement learning (RL) for enhanced performance.
  • Existing research highlights RL's potential in controlling fixed-wing unmanned aerial vehicles (UAVs).
  • A gap exists in the comparative analysis of continuous-space RL algorithms for UAV flight control.

Purpose of the Study:

  • To conduct a comparative analysis of leading continuous-space RL algorithms for fixed-wing UAV flight control.
  • To evaluate the suitability of Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO), and Soft Actor-Critic (SAC) algorithms.
  • To assess algorithm performance in dynamic and uncertain environments, considering UAV pitch, roll, and heading control.

Main Methods:

  • Trained five prominent RL algorithms (DDPG, TD3, PPO, TRPO, SAC) in a high-fidelity simulation environment.
  • Evaluated UAV flight control performance under varying conditions and environmental disturbances (e.g., wind gusts).
  • Compared RL algorithm performance against classical Proportional-Integral-Derivative (PID) controllers.

Main Results:

  • RL algorithms significantly outperformed PID controllers in stability, responsiveness, and robustness.
  • The Soft Actor-Critic (SAC) algorithm achieved convergence within 400 episodes.
  • SAC maintained a steady-state error below 3%, demonstrating the best performance-to-convergence trade-off among evaluated RL algorithms.

Conclusions:

  • Reinforcement learning offers a superior approach to flight control for fixed-wing UAVs compared to traditional methods.
  • The Soft Actor-Critic (SAC) algorithm presents a highly effective and efficient solution for complex UAV flight dynamics.
  • This study provides critical insights for selecting and integrating optimal RL algorithms into advanced UAV control systems.