Online Identification-Verification-Prediction Method for Parallel System Control of UAVs

In order to solve the problem of how to efficiently control a large-scale swarm Unmanned Aerial Vehicle (UAV) system, which performs complex tasks with limited manpower in a non-ideal environment, this paper proposes a parallel UAV swarm control method. The key technology of parallel control is to establish a one-to-one artificial UAV system corresponding to the aerial swarm UAV on the ground. This paper focuses on the computational experiments algorithm for artificial UAV system establishment, including data processing, model identification, model verification and state prediction. Furthermore, this paper performs a comprehensive flight mission with four common modes (climbing, level flighting, turning and descending) for verification. The results of the identification experiment present a good consistency between the outputs of the refined dynamics model and the real flight data. The prediction experiment results show that the prediction method in this paper can basically guarantee that the prediction states error is kept within 10% about 16 s.