Consensus of High-Order Multiagent Systems With Binary-Valued Communications and Switching Topologies

This article studies the consensus problem of high-order multiagent systems (MASs) with binary-valued communications and switching topologies. To tackle the challenge of unknown states caused by binary-valued communications, this article constructs an estimation-based consensus algorithm. First, a recursive projection identification algorithm is presented to estimate the neighbors' states dynamically. Then, based on these estimates, a consensus law is designed. By constructing and analyzing two combined Lyapunov functions about estimation error and state error, this article establishes their relation to overcome the difficulty resulting from the coupling of the estimation and control and less information due to switching topologies. Under the condition of jointly connected topologies, it is proven that by properly selecting the step coefficient, the estimates of states can converge to the true states with a convergence rate as the reciprocal of the recursion times. Besides, the MAS is proved to achieve weak consensus and the consensus rate is also established as the reciprocal of the recursion times. Finally, a simulation example is given to validate the algorithm.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Control of Network Systems
Published
2024-12-12
DOI
https://doi.org/10.1109/tcns.2024.3516582
Citations
5
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
1.74

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Consensus of High-Order Multiagent Systems With Binary-Valued Communications and Switching Topologies

Yanlong Zhao, Ying Wang, Ji‐Feng Zhang, Ruihua An
5 citations
IEEE Transactions on Control of Network Systems
Distributed Control Multi-Agent Systems
1.74
article

Consensus of High-Order Multiagent Systems With Binary-Valued Communications and Switching Topologies

Yanlong Zhao, Ying Wang, Ji‐Feng Zhang, Ruihua An
article en
5 citations

Abstract

This article studies the consensus problem of high-order multiagent systems (MASs) with binary-valued communications and switching topologies. To tackle the challenge of unknown states caused by binary-valued communications, this article constructs an estimation-based consensus algorithm. First, a recursive projection identification algorithm is presented to estimate the neighbors' states dynamically. Then, based on these estimates, a consensus law is designed. By constructing and analyzing two combined Lyapunov functions about estimation error and state error, this article establishes their relation to overcome the difficulty resulting from the coupling of the estimation and control and less information due to switching topologies. Under the condition of jointly connected topologies, it is proven that by properly selecting the step coefficient, the estimates of states can converge to the true states with a convergence rate as the reciprocal of the recursion times. Besides, the MAS is proved to achieve weak consensus and the consensus rate is also established as the reciprocal of the recursion times. Finally, a simulation example is given to validate the algorithm.

IEEE Transactions on Control of Network SystemsVol. 12(2)
Zhongyuan University of Technology (CN), Chinese Academy of Sciences (CN), Academy of Mathematics and Systems Science (CN), KTH Royal Institute of Technology (SE)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Openalex Percentile: Top 14%
Distributed Control Multi-Agent Systems
1.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.