no code implementations • 22 Sep 2022 • Lulu Pan, Haibin Shao, Yang Lu, Mehran Mesbahi, Dewei Li, Yugeng Xi
We show that the vector-valued PPAC problem can be solved via associated matrix-weighted networks with the higher-dimensional agent state.
no code implementations • 28 Aug 2022 • Lulu Pan, Haibin Shao, Mehran Mesbahi, Dewei Li, Yugeng Xi
Inspired by the observation that the link redundancy in a network may degrade its diffusion performance, a distributed data-driven neighbor selection framework is proposed to adaptively adjust the network structure for improving the diffusion performance of exogenous influence over the network.
no code implementations • 26 Oct 2021 • Lulu Pan, Haibin Shao, Yuanlong Li, Dewei Li, Yugeng Xi
The Zeno phenomenon can be excluded for both cases under the proposed coordination strategy.
no code implementations • 26 Sep 2021 • Haibin Shao, Lulu Pan
First, we examine the stability of signed networks by introducing a novel graph-theoretic objective negative cut set, which implies that manipulating negative edge weights cannot change a unstable network into a stable one.
no code implementations • 26 Jul 2021 • Haibin Shao, Lulu Pan, Mehran Mesbahi, Yugeng Xi, Dewei Li
For distributed implementation, a quantitative connection between entries of Laplacian eigenvectors and the "relative rate of change" in the state between neighboring agents is further established; this connection facilitates a distributed algorithm for each agent to identify "favorable" neighbors to interact with.
no code implementations • 20 Jul 2021 • Lulu Pan, Haibin Shao, Mehran Mesbahi, Dewei Li, Yugeng Xi
Second, if the underlying network switches amongst infinite number of networks, the matrix-weighted integral network is employed to provide sufficient conditions for cluster consensus and the quantitative characterization of the corresponding steady-state of the multi-agent system, using null space analysis of matrix-valued Laplacian related of integral network associated with the switching networks.
no code implementations • 11 Jun 2021 • Lulu Pan, Haibin Shao, Dewei Li, Lin Liu
This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network.
no code implementations • 28 Nov 2020 • Chongzhi Wang, Lulu Pan, Haibin Shao, Dewei Li, Yugeng Xi
We show that necessary and/or sufficient conditions for bipartite consensus on matrix-weighted networks can be characterized by the uniqueness of the non-trivial balancing set, while the contribution of the associated non-trivial intersection of null spaces to the steady-state of the matrix-weighted network is examined.