Distributed Estimation in Network Systems Using Event-Driven Receding Horizon Control
Distributed Estimation in Network Systems Using Event-Driven Receding Horizon Control
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DOI：
10.1109/tac.2022.3219285
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发表时间：
2023-09
影响因子：
6.8
通讯作者：
Shirantha Welikala;C. Cassandras
中科院分区：
文献类型：
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作者：
Shirantha Welikala;C. Cassandras
我们考虑的问题，通过一个团队的合作代理（传感器）持续访问的节点，使估计误差协方差估计在一个有限的时间内的整体措施最小化的分布式网络的节点（目标）的状态估计。我们制定这作为一个多智能体的持续监测问题，其目标是控制每个代理的轨迹定义为一个序列的目标访问和相应的停留时间在每个访问的目标进行观察。一个分布式的在线代理控制器的开发，每个代理解决了一系列的滚动时域控制问题（RHCPs）的事件驱动的方式。提出了一种新的目标函数，以优化这种分布式估计过程的有效性，并在一定的假设下建立了其单峰性。此外，提出了一种机器学习解决方案，通过利用每个代理的轨迹的历史来提高这种分布式估计过程的计算效率。最后，提供了大量的数值结果表明显着的改进相比，其他国家的最先进的代理控制器。
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 multiagent 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 online 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.