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.

中文翻译：

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具有神经网络的多无人机系统的分布式有限内存在线学习策略


在本文中，提出了一种新颖的分布式有限内存在线学习（DFMOL）策略，用于准确识别多个无人机（UAV）的动态模型。所提出的 DFMOL 为每个无人机推导了一个循环神经网络（RNN）模型，并在此基础上重新设计了反映相邻无人机信息和连接性的分布式测量模型。所提出的 DFMOL 策略将分布式测量堆叠在后退的地平线上，并仅使用有限数量的分布式测量来估计每个 RNN 的权重，从而确保基于有限内存特性的鲁棒性能。学习律的增益是通过建立弗罗贝尼乌斯范数最小化问题来导出的，以最小化来自无人机及其邻居的干扰、系统不确定性和识别错误的影响。此外，所提出的 DFMOL 策略是迭代导出的，以减少实时硬件实现的计算量。进行实时实验以证明所提出的分布式识别的鲁棒性和准确性。