Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics
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 Lyap...
| Authors | Amos Alwala, Gabriel da Silva Lima, Wallace Moreira Bessa |
|---|---|
| Primary category | cs.RO |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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