mlsanityData to 14 Oct 2026

Deep Q-Learning-Based Gain Scheduling for Nonlinear Quadcopter Dynamics

arXiv 2603.03127 ↗First posted 3 Mar 2026eess.SY cs.SY math.DScited by 0

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

Abstract (arXiv)

This paper presents a deep Q-network (DQN)-based gain-scheduling framework for safety-critical quadcopter trajectory tracking. Instead of directly learning control inputs, the proposed approach selects from a finite set of pre-certified stabilizing gain vectors, enabling reinforcement learning to operate within a structured and stability-preserving control architecture. By exploiting the isotropic structure of the translational dynamics, feedback gains are shared across spatial axes to reduce dimensionality while preserving performance. The learned policy adapts feedback aggressiveness in real time, applying high authority during large transients and reducing gains near convergence to limit control effort. Simulation results using a high-fidelity nonlinear quadcopter model demonstrate accurate trajectory tracking, bounded attitude excursions, smooth transition to hover after the final time, and consistent reward improvement, validating the effectiveness and robustness of the proposed learning-based gain scheduling strategy.

Key facts. No citing papers in the map yet (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 10 of its 22 references that are in the map. First posted on arXiv 3 Mar 2026.

Builds on

10 of 22 references.

References · 10

  1. 2017Constrained Policy Optimizationcited by 983
  2. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
  3. 2018Reinforcement Learning for UAV Attitude Controlcited by 48
  4. 2023DATT: Deep Adaptive Trajectory Tracking for Quadrotor Controlcited by 16
  5. 2023End-to-end Neural Network Based Quadcopter controlcited by 11
  6. 2016Deep Neural Networks for Improved, Impromptu Trajectory Tracking of Quadrotorscited by 9
  7. 2019Online Deep Learning for Improved Trajectory Tracking of Unmanned Aerial Vehicles Using Expert Knowledgecited by 3
  8. 2017An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamicscited by 2
  9. 2021Safe Affine Transformation-Based Guidance of a Large-Scale Multi-Quadcopter System (MQS)cited by 2
  10. 2022Quadcopter Tracking Using Euler-Angle-Free Flatness-Based Controlcited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control
  2. 2026Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics
  3. 2024Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control
  4. 2025Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning
  5. 2025Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy
  6. 2024Deep Reinforcement Learning-based Quadcopter Controller: A Practical Approach and Experiments
  7. 2024Robust Optimal Safe and Stability Guaranteeing Reinforcement Learning Control for Quadcopter ↗
  8. 2026UAV Trajectory Optimization via Improved Noisy Deep Q-Network