What is it about?
Matrix-weighted graphs Matrix-weighted consensus Algebraic conditions for reaching consensus on matrix-weighted graphs Algorithm to investigate clustering behaviors Applications in rendezvous and bearing-constrained formation control problems
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Why is it important?
The paper is the first one to study consensus with nonnegative matrix weights. The cluster consensus achieved by matrix-weighted consensus is novel. Matrix-weighted consensus has applications in network localization, formation control, attitude synchronization, modeling social networks.
Perspectives
Many distributed/decentralized control problems lead to a consensus protocol with matrix weights. The matrix weights can be the rotation matrices, projection matrices, etc. A unique feature of matrix-weighted consensus is clustering phenomenon, which can happen even though the graph is connected.
Dr. Minh Hoang Trinh
Hanoi University of Science and Technology
Read the Original
This page is a summary of: Matrix-weighted consensus and its applications, Automatica, March 2018, Elsevier,
DOI: 10.1016/j.automatica.2017.12.024.
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