mlsanityData to 14 Oct 2026

Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control

arXiv 2604.16819 ↗First posted 18 Apr 2026eess.SY cs.SYcited by 0

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

Abstract (arXiv)

This paper presents an online reinforcement-learning framework for safe gain scheduling of a nonlinear quadcopter controller. Rather than learning thrust and torque commands directly, the proposed method selects gain vectors online from a finite library of pre-certified stabilizing controllers, thereby preserving the structure of the underlying snap-based control law. Safety is enforced by restricting the policy to admissible gains that maintain forward invariance of a prescribed safe state set, while dwell-time constraints prevent excessively fast switching. To reduce the action-space dimension, translational gains are shared across spatial axes by exploiting the isotropic structure of the translational dynamics, whereas yaw gains are scheduled independently. A deep Q-network learns to adjust feedback authority according to the current flight condition, using aggressive gains during large transients and milder gains near hover. High-fidelity nonlinear simulations demonstrate accurate trajectory tracking, bounded attitude motion, reduced control effort near convergence, and stable hover regulation under online safe gain scheduling.

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 11 of its 23 references that are in the map. First posted on arXiv 18 Apr 2026.

Builds on

11 of 23 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control10 Oct
  2. 2024Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight18 Mar

Other references · 9

  1. 2017Constrained Policy Optimizationcited by 983
  2. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
  3. 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 59
  4. 2018Reinforcement Learning for UAV Attitude Controlcited by 48
  5. 2021A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flightcited by 24
  6. 2016Deep Neural Networks for Improved, Impromptu Trajectory Tracking of Quadrotorscited by 9
  7. 2019Inverting Learned Dynamics Models for Aggressive Multirotor Controlcited by 2
  8. 2021Safe Affine Transformation-Based Guidance of a Large-Scale Multi-Quadcopter System (MQS)cited by 2
  9. 2022Quadcopter Tracking Using Euler-Angle-Free Flatness-Based Controlcited by 2

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Close in wording, no citation either way

  1. 2026Deep Q-Learning-Based Gain Scheduling for Nonlinear Quadcopter Dynamics
  2. 2024Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control
  3. 2026Adaptive Outer-Loop Control of Quadrotors via Reinforcement Learning
  4. 2026Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns
  5. 2026Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics
  6. 2026CALOS: Control-Affine Lyapunov On-manifold Safety Layer for Safe Deep Reinforcement Learning for Quadrotors
  7. 2024A Learning-based Quadcopter Controller with Extreme Adaptation
  8. 2025Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning