Radial Basis Function Neural Networks for Formation Control of Unmanned Aerial Vehicles

August 10, 2024 · View on GitHub

The proposed RBF-BSMC to deal with External Disturbance for a team of Multiple UAVs flying in Formation. This repository presents the following article in MATLAB:

Duy-Nam Bui, and Manh Duong Phung, "Radial basis function neural networks for formation control of unmanned aerial vehicles," Robotica, 2024. [Robotica] [Citation]

Citation

@article{Bui2024,
  title = {Radial basis function neural networks for formation control of unmanned aerial vehicles},
  ISSN = {1469-8668},
  url = {http://dx.doi.org/10.1017/S0263574724000559},
  DOI = {10.1017/s0263574724000559},
  journal = {Robotica},
  publisher = {Cambridge University Press (CUP)},
  author = {Bui,  Duy-Nam and Phung,  Manh Duong},
  year = {2024},
  month = apr,
  pages = {1–19}
}

Installation

git clone git@github.com:duynamrcv/rbf_bsmc.git

Run demo

Firstly, run file parameter.m to load the essential parameters. Then open adaptive.slx and press Run in the simulink.

Results

Simulation Results: video

Scenario 1

Top view	Side view

Scenario 2

Top view	3D view

Experiment Results: video

Generate a Standalone ROS Node from Simulink

To develop and deploy controllers on ROS, please follow this guideline