mlsanityData to 7 Oct 2026

Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments

arXiv 2111.04153 ↗First posted 7 Nov 2021eess.SY cs.LG cs.ROcited by 4

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Abstract (arXiv)

Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitudinal and lateral motions. Current state-of-the-art autopilots are based on linear control and are thus limited in their effectiveness and performance. Deep reinforcement learning (DRL) is a machine learning method to automatically discover optimal control laws through interaction with the controlled system, which can handle complex nonlinear dynamics. We show in this paper that DRL can successfully learn to perform attitude control of a fixed-wing UAV operating directly on the original nonlinear dynamics, requiring as little as three minutes of flight data. We initially train our model in a simulation environment and then deploy the learned controller on the UAV in flight tests, demonstrating comparable performance to the state-of-the-art ArduPlane proportional-integral-derivative (PID) attitude controller with no further online learning required. Learning with significant actuation delay and diversified simulated dynamics were found to be crucial for successful transfer to control of the real UAV. In addition to a qualitative comparison with the ArduPlane autopilot, we present a quantitative assessment based on linear analysis to better understand the learning controller's behavior.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 62 references that are in the map. First posted on arXiv 7 Nov 2021.

Builds on

14 of 62 references.

References · 14

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  3. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  4. 2017Hindsight Experience Replaycited by 1,096
  5. 2018Learning Dexterous In-Hand Manipulationcited by 643
  6. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 616
  7. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  8. 2019Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewardscited by 58
  9. 2020Regularizing Action Policies for Smooth Control with Reinforcement Learningcited by 47
  10. 2018Reinforcement Learning for UAV Attitude Controlcited by 40
  11. 2019Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learningcited by 40
  12. 2019Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimizationcited by 14
2 more
  1. 2020How to Train your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learningcited by 5
  2. 2019Neuroflight: Next Generation Flight Control Firmwarecited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraftcited by 1
  2. 2024Model-Free versus Model-Based Reinforcement Learning for Fixed-Wing UAV Attitude Control Under Varying Wind Conditionscited by 0
  3. 2026Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Modelscited by 0
  4. 2026Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entrycited by 0