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Found 8 papers, 0 papers with code
Vector-valued Privacy-Preserving Average Consensus
We show that the vector-valued PPAC problem can be solved via associated matrix-weighted networks with the higher-dimensional agent state.
Structural Adaptivity of Directed Networks
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.
A co-design method of online learning SMC law via an input-mappping strategy
The major novelty of the proposed input-mapping sliding mode control strategy lies in that the sliding mode surface and the sliding mode controller are co-designed through online learning from historical input-output data to minimize an objective function.
Event-triggered Consensus of Matrix-weighted Networks Subject to Actuator Saturation
The Zeno phenomenon can be excluded for both cases under the proposed coordination strategy.
Distributed Neighbor Selection in Multi-agent Networks
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.
Cluster Consensus on Matrix-weighted Switching Networks
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.
Distributed Subgradient Algorithm for Multi-Agent Optimization With Dynamic Stepsize
In this paper, we consider distributed convex optimization problems on multi-agent networks.
Characterizing Bipartite Consensus on Signed Matrix-Weighted Networks via Balancing Set
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.
