Nonlinear System Identification of Swarm of UAVs Using Deep Learning
  Methods
Saman Yazdannik Morteza Tayefi Mojtaba Farrokh
This study designs and evaluates multiple nonlinear system identification
techniques for modeling the UAV swarm system in planar space. learning methods
such as RNNs, CNNs, and Neural ODE are explored and compared. The objective is
to forecast future swarm trajectories by accurately approximating the nonlinear
dynamics of the swarm model. The modeling process is performed using both
transient and steady-state data from swarm simulations. Results show that the
combination of Neural ODE with a well-trained model using transient data is
robust for varying initial conditions and outperforms other learning methods in
accurately predicting swarm stability.