TY - GEN
T1 - Event-Driven Receding Horizon Control for Distributed Estimation in Network Systems
AU - Welikala, Shirantha
AU - Cassandras, Christos G.
N1 - Publisher Copyright:
© 2021 American Automatic Control Council.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85111939740&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111939740&partnerID=8YFLogxK
U2 - 10.23919/ACC50511.2021.9483147
DO - 10.23919/ACC50511.2021.9483147
M3 - Conference contribution
AN - SCOPUS:85111939740
T3 - Proceedings of the American Control Conference
SP - 1559
EP - 1564
BT - 2021 American Control Conference, ACC 2021
T2 - 2021 American Control Conference, ACC 2021
Y2 - 25 May 2021 through 28 May 2021
ER -