# Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics

arXiv: https://arxiv.org/abs/2606.31199 · HTML: https://mlsanity.com/paper/2606.31199/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 4 of 4)

- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Modelling, identification and geometric control of autonomous quadcopters for agile maneuvering](https://mlsanity.com/paper/2306.09651/) (2023)
- [Radial Basis Function Neural Networks for Formation Control of Unmanned Aerial Vehicles](https://mlsanity.com/paper/2404.13583/) (2024)
- [DQ-NMPC: Dual-Quaternion NMPC for Quadrotor Flight](https://mlsanity.com/paper/2510.11525/) (2025)

## Abstract (arXiv)

The control of agile quadrotors in dynamic and uncertain environments remains an open area of investigation to this day, particularly when the complete system dynamics are partially known or highly nonlinear. This work introduces a novel machine learning-based feedback-linearization control framework that employs a Gaussian Radial Basis Function (RBF) neural network (NN) to model and compensate for unmodeled dynamics in real time. The proposed controller leverages the universal approximation capability of RBF networks to model nonlinearities and uncertainties. An online adaptation of the RBF NN updates the network's weights without prior training. The control law is derived using the Lyapunov stability theory, herein guaranteeing closed-loop stability and providing theoretical guarantee of asymptotic convergence of a trajectory tracking task. Gazebo simulation and real flight experiments are conducted using the Bitcraze's Crazyflie 2.1 quadrotor subject to unmodeled air drag, actuator dynamics, and external disturbance. Despite incomplete knowledge of prior dynamics and presence of external disturbance such as air drag and drift in state estimation, the proposed controller improves trajectory tracking with rapid convergence and reduction of position-norm and yaw orientation RMSE by more than 7.13% and 49.27% respectively compared to baseline feedback linearization controller.