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Radial basis function neural networks for formation control of unmanned aerial vehicles

Published online by Cambridge University Press:  19 April 2024

Duy-Nam Bui Open the ORCID record for Duy-Nam Bui [Opens in a new window]  and
Manh Duong Phung Open the ORCID record for Manh Duong Phung [Opens in a new window]
Duy-Nam Bui
Affiliation:
Vietnam National University, Hanoi, Vietnam
Manh Duong Phung*
Affiliation:
Fulbright University Vietnam, Ho Chi Minh City, Vietnam
*
Corresponding author: Manh Duong Phung; Email: duong.phung@fulbright.edu.vn
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Abstract

This paper addresses the problem of controlling multiple unmanned aerial vehicles (UAVs) cooperating in a formation to carry out a complex task such as surface inspection. We first use the virtual leader-follower model to determine the topology and trajectory of the formation. A double-loop control system combining backstepping and sliding mode control techniques is then designed for the UAVs to track the trajectory. A radial basis function neural network capable of estimating external disturbances is developed to enhance the robustness of the controller. The stability of the controller is proven by using the Lyapunov theorem. A number of comparisons and software-in-the-loop tests have been conducted to evaluate the performance of the proposed controller. The results show that our controller not only outperforms other state-of-the-art controllers but is also sufficient for complex tasks of UAVs such as collecting surface data for inspection. The source code of our controller can be found at https://github.com/duynamrcv/rbf_bsmc.

Keywords

unmanned aerial vehicles (UAVs)formation controlradial basis function neural networkbackstepping sliding mode control
Type
Research Article
Information
Robotica , First View , pp. 1 - 19
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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