Refinement of UAV dynamics model through online identification: a model-data hybrid approach
Yixin Huang, Xiaojia Xiang, Han Zhou, Dengqing Tang, Yuan Chang · 2020
This paper investigates how to precisely and efficiently obtain the dynamics model of Unmanned Aerial Vehicle (UAV) which can used in single or swarm control. The UAVs are homogeneous in the mechanical structure, yet differ in a variety of unmodeled uncertainties, which cannot be ignored in high-fidelity simulation systems. To solve this, we propose a model-data hybrid approach that consists of two steps: 1) establishing a baseline model by first-principle; and 2) refining the model through online identification. More specifically, the baseline model is a linearized state equation with pending parameters. The parameters are adaptively determined through a least-squares identification algorithm based on real-time flight data. Finally, we design a comprehensive flight mission with 5 common modes (climbing, level flighting, turning, descending, and circling) for verification. The results of simulation experiment present a good consistency between the outputs of the refined dynamics model and the real flight data.