A sensing and control closed-loop scheduling strategy for UAV swarms

Driven by technologies such as the Internet of things (IoT), edge computing, and autonomous positioning and navigation, UAV swarms have been widely applied in scenarios such as emergency communication, data collection, environmental mapping, and intelligent logistics. To achieve optimal system performance under limited communication resources, a remote control framework for UAV swarms was designed. The framework considered the differences in external environments where UAV operate and integrates state estimation errors and control errors to formulate a joint optimization problem for control commands and scheduling decisions. This problem was decomposed into two subproblems: robust controller design and update scheduling, which were solved using quadratic programming and Lyapunov optimization methods, respectively. Simulation results verify the importance of prior contextual information and control errors in UAV swarm scheduling. Compared with traditional strategies that only consider state estimation errors, the new method reduces tracking errors by up to 13.74% and demonstrates better performance under various state transition noise conditions.