Event-Driven Receding Horizon Control for Distributed Estimation in Network Systems

This paper considers the multi-agent persistent monitoring problem defined on a network (graph) of nodes (targets) with independent uncertain states. The agent team's goal is to persistently observe the target states so that an overall measure of estimation error covariance evaluated over a finite period is minimized. Each agent's trajectory is fully defined by the sequence of targets it visits and the corresponding dwell times spent at each visited target. To find the optimal set of agent trajectories for this estimation task over the network, we develop a distributed on-line agent controller that requires each agent to solve a sequence of receding horizon control problems (RHCPs) in an event-driven manner. We use a novel objective function form for these RHCPs to optimize the effectiveness of this distributed estimation process and establish its unimodality under certain conditions. Finally, extensive numerical results are provided, indicating significant improvements compared to other agent control methods.