mlsanityData to 14 Oct 2026

AirPilot: Interpretable PPO-based DRL Auto-Tuned Nonlinear PID Drone Controller for Robust Autonomous Flights

arXiv 2404.00204 ↗First posted 30 Mar 2024cs.RO cs.LG cs.SYcited by 2

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Navigation precision, speed and stability are crucial for safe Unmanned Aerial Vehicle (UAV) flight maneuvers and effective flight mission executions in dynamic environments. Different flight missions may have varying objectives, such as minimizing energy consumption, achieving precise positioning, or maximizing speed. A controller that can adapt to different objectives on the fly is highly valuable. Proportional Integral Derivative (PID) controllers are one of the most popular and widely used control algorithms for drones and other control systems, but their linear control algorithm fails to capture the nonlinear nature of the dynamic wind conditions and complex drone system. Manually tuning the PID gains for various missions can be time-consuming and requires significant expertise. This paper aims to revolutionize drone flight control by presenting the AirPilot, a nonlinear Deep Reinforcement Learning (DRL) - enhanced Proportional Integral Derivative (PID) drone controller using Proximal Policy Optimization (PPO). AirPilot controller combines the simplicity and effectiveness of traditional PID control with the adaptability, learning capability, and optimization potential of DRL. This makes it better suited for modern drone applications where the environment is dynamic, and mission-specific performance demands are high. We employed a COEX Clover autonomous drone for training the DRL agent within the simulator and implemented it in a real-world lab setting, which marks a significant milestone as one of the first attempts to apply a DRL-based flight controller on an actual drone. Airpilot is capable of reducing the navigation error of the default PX4 PID position controller by 90%, improving effective navigation speed of a fine-tuned PID controller by 21%, reducing settling time and overshoot by 17% and 16% respectively.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 3 references that are in the map. First posted on arXiv 30 Mar 2024.

Builds on

2 of 3 references.

References · 2

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Control of a Quadrotor with Reinforcement Learningcited by 92

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Trackingcited by 6
  2. 2025Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Controlcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs
  2. 2024Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter System
  3. 2025Methods for Multi-objective Optimization PID Controller for quadrotor UAVs
  4. 2025Autonomous Drone for Dynamic Smoke Plume Tracking ↗
  5. 2025AgilePilot: DRL-Based Drone Agent for Real-Time Motion Planning in Dynamic Environments by Leveraging Object Detection
  6. 2025Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning
  7. 2025Sim-to-Real Transfer in Reinforcement Learning for Maneuver Control of a Variable-Pitch MAV
  8. 2025Rethinking Reference Trajectories in Agile Drone Racing: A Unified Reference-Free Model-Based Controller via MPPI