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A strategy for applying digital twin (DT) tool has been proposed to support decentral-ized unmanned aerial vehicle (UAV) swarm control mechanisms, which can ensure: scala-ble and stable UAV operation in changing conditions; autonomous decision-making in conditions of limited or unstable communication; adaptation of UAV behavior based on available analytics. The main task of the ground station's DT is to provide comprehensive modeling, testing, planning, and adaptation of the UAV swarm to complex dynamic environmental conditions. An onboard interface module integrated into each drone ensures autonomous operation of the system in conditions of partial or complete loss of communica-tion with the ground infrastructure. It implements local adaptation to environmental changes, performs internal diagnostics of the technical condition of systems, and recon-structs the environment using onboard sensors and odometry or SLAM algorithms. The use of DT models makes it possible to transfer a significant part of the computational load from onboard AI platforms to a ground station, while maintaining strategic coordination, predictability, and flexibility of the collective behavior of the UAV swarm. A practical scenario for the application of DT in UAV swarm systems is presented, demonstrating its role as the strategic core of the control system.