Noncooperative Formation Tracking of UAVs Against Multiple Cyber-Threats: A Twin-Network Approach

In this article, we investigate the problem of unmanned aerial vehicles (UAVs) formation tracking in the presence of multiple cyber-threats. In this scenario, UAVs have private, potentially conflicting objectives, and their communication networks and local feedback mechanisms are vulnerable to sabotage and eavesdropping by malicious attackers. To address this real-world challenge, we propose a control scheme based on a twin-network structure. Specifically, we construct a virtual twin layer interconnected with the physical layer to design a resilient estimator that fortifies information exchange among UAVs under threats. In addition, by coupling the states from the twin layer and time-varying signals as masks, we achieve privacy protection for critical information. Furthermore, leveraging reliable data provided by the resilient estimator, we design a cooperative controller based on gradient-optimization to update the UAVs' positions. Using Lyapunov theory, we prove that the position of all UAVs converge to a dynamic Nash equilibrium. Finally, we conduct experimental studies to validate the effectiveness of the proposed control scheme.