30,448 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
Cooperative H-infinity Estimation for Large-Scale Interconnected Linear Systems
In this paper, a synthesis method for distributed estimation is presented,
which is suitable for dealing with large-scale interconnected linear systems
with disturbance. The main feature of the proposed method is that local
estimators only estimate a reduced set of state variables and their complexity
does not increase with the size of the system. Nevertheless, the local
estimators are able to deal with lack of local detectability. Moreover, the
estimators guarantee H-infinity-performance of the estimates with respect to
model and measurement disturbances.Comment: Short version published in Proc. American Control Conference (ACC),
pp.2119-2124. Chicago, IL, 201
Consensus Computation in Unreliable Networks: A System Theoretic Approach
This work addresses the problem of ensuring trustworthy computation in a
linear consensus network. A solution to this problem is relevant for several
tasks in multi-agent systems including motion coordination, clock
synchronization, and cooperative estimation. In a linear consensus network, we
allow for the presence of misbehaving agents, whose behavior deviate from the
nominal consensus evolution. We model misbehaviors as unknown and unmeasurable
inputs affecting the network, and we cast the misbehavior detection and
identification problem into an unknown-input system theoretic framework. We
consider two extreme cases of misbehaving agents, namely faulty (non-colluding)
and malicious (Byzantine) agents. First, we characterize the set of inputs that
allow misbehaving agents to affect the consensus network while remaining
undetected and/or unidentified from certain observing agents. Second, we
provide worst-case bounds for the number of concurrent faulty or malicious
agents that can be detected and identified. Precisely, the consensus network
needs to be 2k+1 (resp. k+1) connected for k malicious (resp. faulty) agents to
be generically detectable and identifiable by every well behaving agent. Third,
we quantify the effect of undetectable inputs on the final consensus value.
Fourth, we design three algorithms to detect and identify misbehaving agents.
The first and the second algorithm apply fault detection techniques, and
affords complete detection and identification if global knowledge of the
network is available to each agent, at a high computational cost. The third
algorithm is designed to exploit the presence in the network of weakly
interconnected subparts, and provides local detection and identification of
misbehaving agents whose behavior deviates more than a threshold, which is
quantified in terms of the interconnection structure
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