论文标题
事件驱动的向后水平控制控制网络系统中的分布式估计
Event-Driven Receding Horizon Control for Distributed Estimation in Network Systems
论文作者
论文摘要
我们考虑了通过持续访问节点的合作代理（传感器）团队来估计节点（目标）分布式网络（目标）的状态的问题，以便最大程度地评估了在有限周期中评估的估计误差协方差的总体测量。我们将其作为多代理的持续监视问题提出，其中目标是控制每个代理的轨迹定义为目标访问序列，并且在每个访问的目标上花费了相应的停留时间。开发了分布式的在线代理控制器，每个代理以事件驱动的方式求解一系列退缩的地平线控制问题（RHCP）。为这些RHCP提出了一种新颖的目标函数，以优化该分布式估计过程的有效性，并在某些假设下建立了其非兴趣性能。此外，提出了一种机器学习解决方案，以通过利用每个代理轨迹的历史记录来提高该分布式估计过程的计算效率。最后，提供了广泛的数值结果，表明与其他最先进的代理控制器相比，有显着改善。
We consider the problem of estimating the states of a distributed network of nodes (targets) through a team of cooperating agents (sensors) persistently visiting the nodes so that an overall measure of estimation error covariance evaluated over a finite period is minimized. We formulate this as a multi-agent persistent monitoring problem where the goal is to control each agent's trajectory defined as a sequence of target visits and the corresponding dwell times spent making observations at each visited target. A distributed on-line agent controller is developed where each agent solves a sequence of receding horizon control problems (RHCPs) in an event-driven manner. A novel objective function is proposed for these RHCPs so as to optimize the effectiveness of this distributed estimation process and its unimodality property is established under some assumptions. Moreover, a machine learning solution is proposed to improve the computational efficiency of this distributed estimation process by exploiting the history of each agent's trajectory. Finally, extensive numerical results are provided indicating significant improvements compared to other state-of-the-art agent controllers.
