19 research outputs found
A Combinatorial Necessary and Sufficient Condition for Cluster Consensus
In this technical note, cluster consensus of discrete-time linear multi-agent
systems is investigated. A set of stochastic matrices is said to
be a cluster consensus set if the system achieves cluster consensus for any
initial state and any sequence of matrices taken from . By
introducing a cluster ergodicity coefficient, we present an equivalence
relation between a range of characterization of cluster consensus set under
some mild conditions including the widely adopted inter-cluster common
influence. We obtain a combinatorial necessary and sufficient condition for a
compact set to be a cluster consensus set. This combinatorial
condition is an extension of the avoiding set condition for global consensus,
and can be easily checked by an elementary routine. As a byproduct, our result
unveils that the cluster-spanning trees condition is not only sufficient but
necessary in some sense for cluster consensus problems.Comment: 6 page
Cluster-based informed agents selection for flocking with a virtual leader
2014-2015 > Academic research: refereed > Refereed conference paperAccepted ManuscriptPublishe
H
An H∞ consensus problem of multiagent systems is studied by introducing disturbances into the systems. Based on H∞ control theory and consensus theory, a condition is derived to guarantee the systems both reach consensus and have a certain H∞ property. Finally, an example is worked out to demonstrate the effectiveness of the theoretical results
Cluster Consensus on Discrete-Time Multi-Agent Networks
Nowadays, multi-agent networks are ubiquitous in the real world. Over the last decade, consensus has received an increasing attention from various disciplines. This paper investigates cluster consensus for discrete-time multi-agent networks. By utilizing a special coupling matrix and the Kronecker product, a criterion based on linear matrix inequality (LMI) is obtained. It is shown that the addressed discrete-time multi-agent networks achieve cluster consensus if a certain LMI is feasible. Finally, an example is given to demonstrate the effectiveness of the proposed criterion
Adaptive Synchronization of Nonlinearly Parameterized Complex Dynamical Networks with Unknown Time-Varying Parameters
A new adaptive learning control approach is proposed for a class of nonlinearly parameterized complex dynamical networks with unknown time-varying parameters. By using the parameter separation and reparameterization technique, the adaptive learning laws of periodically time-varying and constant parameters and an adaptive control strategy are designed to ensure the asymptotic convergence of the synchronization error in the sense of square error norm. Then, a sufficient condition of the synchronization is given by constructing a composite energy function. Finally, an example of the complex network is used to verify the effectiveness of proposed approach
Evolution and maintenance of cooperation via inheritance of spatial neighbourhood
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