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Solving the N-Consensus Problem: Combining Clustering and Synchronization

Gering, Stefan ; Willert, Volker (2023)
Solving the N-Consensus Problem: Combining Clustering and Synchronization.
In: IFAC Proceedings Volumes, 2012, 45 (26)
doi: 10.26083/tuprints-00023317
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Solving the N-Consensus Problem: Combining Clustering and Synchronization
Language: English
Date: 2023
Place of Publication: Darmstadt
Year of primary publication: 2012
Publisher: IFAC - International Federation of Automatic Control
Journal or Publication Title: IFAC Proceedings Volumes
Volume of the journal: 45
Issue Number: 26
DOI: 10.26083/tuprints-00023317
Corresponding Links:
Origin: Secondary publication service

This article presents a state synchronization method within multi-agent systems upon multiple states. Based on their formation in state space, the agents decide on a clustering and synchronize their states within these clusters. The solution steps for this N-consensus problem, clustering and synchronization, may both be solved entirely in a decentral manner. This is achieved by means of a distributed Variational Bayes to describe the distribution of the agents' positions as a mixture of densities. The entire N-consensus problem is illustrated with graphical probabilistic models whose underlying potential is shown to be maximized when reaching the final N-consensus. An improvement of the overall convergence speed is achieved by a dynamical adaption of the distributed Variational Bayes, which leads to an intertwining of clustering and synchronization.

Uncontrolled Keywords: Autonomous mobile robots, consensus, distributed control, probabilistic models
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-233178
Additional Information:

Zugl. Konferenzveröffentlichung: 3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems, 14.-15.09.2012, Santa Barbara, USA

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Intelligent Systems
Date Deposited: 10 Mar 2023 10:09
Last Modified: 26 May 2023 07:41
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23317
PPN: 507994248
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