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

Event-Triggered Optimal Formation Tracking Control Using Reinforcement Learning for Large-Scale UAV Systems