.png){kind=icon}
Return
Average consensus on general strongly connected digraphs
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2012.08.003.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We study the average consensus problem of multi-agent systems for general network topologies with unidirectional information flow. We propose two linear distributed algorithms, deterministic and gossip, respectively for the cases where the inter-agent communication is synchronous and asynchronous. In both cases, the developed algorithms guarantee state averaging on arbitrary strongly connected digraphs; in particular, this graphical condition does not require that the network be balanced or symmetric, thereby extending previous results in the literature. The key novelty of our approach is to augment an additional variable for each agent, called surplus, whose function is to locally record individual state updates. For convergence analysis, we employ graph-theoretic and nonnegative matrix tools, plus the eigenvalue perturbation theory playing a crucial role. (c) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Multi-agent consensus
Surplus-based averaging
Gossip algorithm
Directed graph
Eigenvalue perturbation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSaveShareSaveShareSave
View more