Large-scale UAV switching formation tracking control has been widely applied in many fields such as search and rescue, cooperative transportation, and UAV light shows. In order to optimize the control performance and reduce the computational burden of the system, this study proposes an event-triggered optimal formation tracking controller for discrete-time large-scale UAV systems (UASs). And an optimal decision - optimal control framework is completed by introducing the Hungarian algorithm and actor-critic neural networks (NNs) implementation. Finally, a large-scale mixed reality experimental platform is built to verify the effectiveness of the proposed algorithm, which includes large-scale virtual UAV nodes and limited physical UAV nodes. This compensates for the limitations of the experimental field and equipment in realworld scenario, ensures the experimental safety, significantly reduces the experimental cost, and is suitable for realizing largescale UAV formation light shows.

翻译：大规模无人机切换编队跟踪控制已广泛应用于搜救、协同运输、无人机灯光秀等多个领域。为优化控制性能并降低系统计算负担，本研究针对离散时间大规模无人机系统（UASs）提出了一种事件触发的最优编队跟踪控制器。通过引入匈牙利算法与执行器-评价器神经网络，构建了最优决策-最优控制框架。最后搭建了包含大规模虚拟无人机节点与有限物理节点的大规模混合现实实验平台，验证了所提算法的有效性。该平台弥补了真实场景中实验场地与设备的局限性，保障了实验安全性，显著降低了实验成本，适用于实现大规模无人机编队灯光秀。