1,161 research outputs found
Decentralized Estimation of Laplacian Eigenvalues in Multi-Agent Systems
In this paper we present a decentralized algorithm to estimate the
eigenvalues of the Laplacian matrix that encodes the network topology of a
multi-agent system. We consider network topologies modeled by undirected
graphs. The basic idea is to provide a local interaction rule among agents so
that their state trajectory is a linear combination of sinusoids oscillating
only at frequencies function of the eigenvalues of the Laplacian matrix. In
this way, the problem of decentralized estimation of the eigenvalues is mapped
into a standard signal processing problem in which the unknowns are the finite
number of frequencies at which the signal oscillates
Hearing the clusters in a graph: A distributed algorithm
We propose a novel distributed algorithm to cluster graphs. The algorithm
recovers the solution obtained from spectral clustering without the need for
expensive eigenvalue/vector computations. We prove that, by propagating waves
through the graph, a local fast Fourier transform yields the local component of
every eigenvector of the Laplacian matrix, thus providing clustering
information. For large graphs, the proposed algorithm is orders of magnitude
faster than random walk based approaches. We prove the equivalence of the
proposed algorithm to spectral clustering and derive convergence rates. We
demonstrate the benefit of using this decentralized clustering algorithm for
community detection in social graphs, accelerating distributed estimation in
sensor networks and efficient computation of distributed multi-agent search
strategies
Tracking control for multi-agent consensus with an active leader and variable topology
In this paper, we consider the coordination control of a group of autonomous
mobile agents with multiple leaders. Different interconnection topologies are
investigated. At first, a necessary and sufficient condition is proved in the
case of fixed interconnection topology. Then a sufficient condition is proposed
when the interconnection topology is switched. With a simple first-order
dynamics model by using the neighborhood rule, both results show that the group
behavior of the agents will converge to the polytope formed by the leaders.Comment: 6 page
Decentralized Event-Triggered Consensus of Linear Multi-agent Systems under Directed Graphs
An event-triggered control technique for consensus of multi-agent systems
with general linear dynamics is presented. This paper extends previous work to
consider agents that are connected using directed graphs. Additionally, the
approach shown here provides asymptotic consensus with guaranteed positive
inter-event time intervals. This event-triggered control method is also used in
the case where communication delays are present. For the communication delay
case we also show that the agents achieve consensus asymptotically and that,
for every agent, the time intervals between consecutive transmissions is
lower-bounded by a positive constant.Comment: 9 pages, 5 figures, A preliminary version of this manuscript has been
submitted to the 2015 American Control Conferenc
Distributed Estimation and Control of Algebraic Connectivity over Random Graphs
In this paper we propose a distributed algorithm for the estimation and
control of the connectivity of ad-hoc networks in the presence of a random
topology. First, given a generic random graph, we introduce a novel stochastic
power iteration method that allows each node to estimate and track the
algebraic connectivity of the underlying expected graph. Using results from
stochastic approximation theory, we prove that the proposed method converges
almost surely (a.s.) to the desired value of connectivity even in the presence
of imperfect communication scenarios. The estimation strategy is then used as a
basic tool to adapt the power transmitted by each node of a wireless network,
in order to maximize the network connectivity in the presence of realistic
Medium Access Control (MAC) protocols or simply to drive the connectivity
toward a desired target value. Numerical results corroborate our theoretical
findings, thus illustrating the main features of the algorithm and its
robustness to fluctuations of the network graph due to the presence of random
link failures.Comment: To appear in IEEE Transactions on Signal Processin
- …