Graph Convolutional Flocking Control for Unmanned Aerial Vehicles With Packet Dropouts

Flocking control of unmanned aerial vehicles (UAVs) plays a vital role in numerous applications with its adaptability, decentralization, and self-organization. Despite advances, the control performance of present approaches will deteriorate in practical applications due to the disregard of the strong coupling of the complexity of the UAVs (e.g., nonlinearity and strong coupling), agent interaction, and network quality (e.g., time delay and packet dropouts). To address this problem, we propose a novel graph convolutional flocking strategy for nonlinear UAV systems in the event of stochastic packet dropouts over the communication network. Different from the existing methods that only rely on a nominal model, this article formulates the flocking control problem from the perspective of an underlying graph, and exploits the graph convolution to generate a contextualized representation of the spatiotemporal state-control space. A predictive model of packet dropouts is generated with the state-control sequences in the learned representation space. Consequently, a graph convolutional flocking controller including a packet dropout predictive compensation module is proposed. The stability of the closed-loop system is verified by Lyapunov theory. Simulation and physical-world experimental studies for flocking control of a multiquadcopter system are performed to verify the effectiveness of our proposed strategy.