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

Shirantha Welikala, Christos G. Cassandras

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2021 American Control Conference, ACC 2021
Pages1559-1564
Number of pages6
ISBN (Electronic)9781665441971
DOIs
StatePublished - 25 May 2021
Event2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States
Duration: 25 May 202128 May 2021

Publication series

NameProceedings of the American Control Conference
Volume2021-May
ISSN (Print)0743-1619

Conference

Conference2021 American Control Conference, ACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans
Period25/05/2128/05/21

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