Adaptive neural network based quadrotor UAV formation control under external disturbances

• Published in: Aerospace science and technology 2024-12, Vol.155, p.109608, Article 109608
• Main Authors: Singha, Arindam, Ray, Anjan Kumar, Govil, Mahesh Chandra
• Format: Article
• Language: English
• Subjects: Adaptive control; Disturbance estimation; Formation control; Quadrotor UAV; Radial basis function neural network; Weighted directed network topology
• ISSN: 1270-9638
• DOI: 10.1016/j.ast.2024.109608

The formation control of a team comprised of multiple quadrotor Unmanned Aerial Vehicles (UAVs) may severely be affected by the unknown external disturbances. The external disturbances are caused by wind forces to create aero-dynamical disturbances. This article addresses the robust formation control problem of multiple UAVs system despite the effect of external disturbances that allow sustaining a stable network connection among the UAVs and maintaining different formations assigned to them. First, a Radial Basis Function Neural Network (RBFNN) based model is developed to reciprocate the external disturbances along the positional and the attitude subsystems. Then incorporating the estimated disturbance values a distributed adaptive formation controller is devised using the Lyapunov theory. It consists of a positional and an attitude controller associated with the translational and the rotational movements of the UAVs. The stability is validated by satisfying the criteria of the Lyapunov stability function. The UAVs are connected through variable adjacency matrix based directed network topology and the network connectivity is established through the properties of the Laplacian Matrix. The robustness of the designed controller is justified via rigorous simulation studies for different sets of desired formations such as triangular, squared, tetrahedron, octahedron and cube shaped. The reference trajectories are considered as spiral, straight line and circular shaped. The time varying external disturbances are considered of sinusoidal waveform of different magnitudes. The simulation results signifies that the proposed RBFNN based formation controller reciprocate different sinusoidal waveforms to achieve the desired formations successfully. Extensive comparative studies demonstrate the efficacy of the proposed adaptive formation controller over the existing controllers presented in the literature for different shapes of trajectories and desired formations. •An adaptive formation controller is designed considering the time-varying external disturbances on multiple UAVs system.•The properties of Laplacian matrix are satisfied for a stable directed network with variable weighted adjacency matrix.•The overall stability of the proposed formation controller is satisfied with the Lyapunov stability function.•The proposed controller is validated with different magnitudes of time-varying external disturbances on individual UAVs.•The proposed formation controller can ac