论文标题
沟通高效的大型无人机在线路径控制：联合学习符合平均场地游戏理论
Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory
论文作者
论文摘要
本文调查了大量无人机等无人机人群的控制。通过考虑它们之间的交互来进行羊群，可以直接控制无人机的方法，这需要一个巨大的UAV沟通，这是在实时应用程序中无法实现的。一种控制的方法是应用平均场游戏（MFG）框架，该框架大大降低了无人机之间的通信。但是，要实现这一框架，需要强大的处理器才能在不同无人机上获得控制法律。该要求限制了MFG框架在实时应用程序（例如大规模无人机控制）中的使用。因此，基于神经网络（NN）的函数近似器可用于近似汉密尔顿 - 雅各比 - 贝尔曼（HJB）和Fokker-Planck-Kolmogorov（FPK）方程的解决方案。但是，使用近似解决方案可能会违反MFG框架收敛条件。因此，与基于NN的MFG提出了可以共享NNS模型参数的联合学习方法（FL）方法以满足所需条件。提出了基于NN的MFG方法的稳定性分析，并通过模拟详细阐述了所提出的FL-MFG的性能。
This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by considering the interactions among them to make a flock requires a huge inter-UAV communication which is impossible to implement in real-time applications. One method of control is to apply the mean-field game (MFG) framework which substantially reduces communications among the UAVs. However, to realize this framework, powerful processors are required to obtain the control laws at different UAVs. This requirement limits the usage of the MFG framework for real-time applications such as massive UAV control. Thus, a function approximator based on neural networks (NN) is utilized to approximate the solutions of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Nevertheless, using an approximate solution can violate the conditions for convergence of the MFG framework. Therefore, the federated learning (FL) approach which can share the model parameters of NNs at drones, is proposed with NN based MFG to satisfy the required conditions. The stability analysis of the NN based MFG approach is presented and the performance of the proposed FL-MFG is elaborated by the simulations.
