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

TL;DR

This paper proposes a robust formation control method for multiple UAVs using a combination of virtual leader-follower topology, backstepping, sliding mode control, and RBF neural networks to estimate disturbances, validated through simulations.

Contribution

It introduces an RBF neural network-based disturbance observer integrated with a backstepping and sliding mode control framework for UAV formation control.

Findings

Outperforms existing controllers in simulations

Successfully handles external disturbances

Proves stability via Lyapunov theorem

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 (RBFNN) 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 (SIL) 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