Distributed Finite Memory Online Learning Strategy for Multi-UAV Systems With Neural Networks

In this article, a novel distributed finite memory online learning (DFMOL) strategy is proposed for accurately identifying the dynamic models of multiple unmanned aerial vehicles (UAVs). The proposed DFMOL derives a recurrent neural network (RNN) model for each UAV, based on which it redesigns a distributed measurement model that reflects the information and connectivity of neighboring UAVs. The proposed DFMOL strategy stacks the distributed measurements on a receding horizon and estimates the weights of each RNN using only a finite number of distributed measurements, ensuring robust performance based on the characteristics of finite memory. The gain of learning law is derived by establishing the Frobenius norm minimization problem to minimize the impact of disturbances, systematic uncertainties, and identification errors from UAVs and their neighbors. Furthermore, the proposed DFMOL strategy is derived iteratively to reduce the computation for real-time hardware implementation. Real-time experiments are carried out to show the robust and accurate performance of the proposed distributed identification.