Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics
arXiv:2606.31199v1 Announce Type: cross Abstract: 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 an
The increasing complexity of autonomous systems and the need for robust control in dynamic environments are driving advancements in machine learning-based control methods.
This development enhances the autonomy and reliability of unmanned aerial vehicles, critical for various applications from logistics to defense, even in unpredictable conditions.
Quadrotors can now better operate in environments where their full dynamics are unknown or highly variable, making them more adaptable and less prone to control failures.
- · Drone manufacturers
- · Logistics companies
- · Defense contractors
- · AI/ML research institutions
- · Traditional control systems developers
- · Competitors with less adaptive autonomous platforms
Improved performance and reliability of quadrotor systems in real-world, dynamic scenarios.
Accelerated adoption of autonomous aerial vehicles across various industries due to enhanced robustness.
The development of highly adaptive, general-purpose autonomous agents capable of navigating entirely novel environments with minimal pre-programming.
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Read at arXiv cs.LG