mlsanityData to 14 Oct 2026

Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control

arXiv 2403.07216 ↗First posted 12 Mar 2024eess.SY cs.LG cs.ROcited by 1

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

Abstract (arXiv)

The paper presents a technique using reinforcement learning (RL) to adapt the control gains of a quadcopter controller. Specifically, we employed Proximal Policy Optimization (PPO) to train a policy which adapts the gains of a cascaded feedback controller in-flight. The primary goal of this controller is to minimize tracking error while following a specified trajectory. The paper's key objective is to analyze the effectiveness of the adaptive gain policy and compare it to the performance of a static gain control algorithm, where the Integral Squared Error and Integral Time Squared Error are used as metrics. The results show that the adaptive gain scheme achieves over 40% decrease in tracking error as compared to the static gain controller.

Key facts. Cited by 1 paper 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 1 of its 7 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

1 of 7 references.

References · 1

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215

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Citing papers per year: 2024: 1.

1 paper.

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  3. 2026Deep Q-Learning-Based Gain Scheduling for Nonlinear Quadcopter Dynamics
  4. 2026A Heuristic Approach for Performance Tuning in RL-based Quadrotor Control via Reward Design and Termination Conditions
  5. 2025Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking
  6. 2025Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs
  7. 2025RAPTOR: A Foundation Policy for Quadrotor Control
  8. 2026Adaptive Outer-Loop Control of Quadrotors via Reinforcement Learning