Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control
Abstract
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.
Cite
@article{arxiv.2604.16819,
title = {Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control},
author = {Muhammad Junayed Hasan Zahed and Chieh Tsai and Salim Hariri and Hossein Rastgoftar},
journal= {arXiv preprint arXiv:2604.16819},
year = {2026}
}