44,290 research outputs found
Distributed two-time-scale methods over clustered networks
In this paper, we consider consensus problems over a network of nodes, where
the network is divided into a number of clusters. We are interested in the case
where the communication topology within each cluster is dense as compared to
the sparse communication across the clusters. Moreover, each cluster has one
leader which can communicate with other leaders in different clusters. The goal
of the nodes is to agree at some common value under the presence of
communication delays across the clusters.
Our main contribution is to propose a novel distributed two-time-scale
consensus algorithm, which pertains to the separation in network topology of
clustered networks. In particular, one scale is to model the dynamic of the
agents in each cluster, which is much faster (due to the dense communication)
than the scale describing the slowly aggregated evolution between the clusters
(due to the sparse communication). We prove the convergence of the proposed
method in the presence of uniform, but possibly arbitrarily large,
communication delays between the leaders. In addition, we provided an explicit
formula for the convergence rate of such algorithm, which characterizes the
impact of delays and the network topology. Our results shows that after a
transient time characterized by the topology of each cluster, the convergence
of the two-time-scale consensus method only depends on the connectivity of the
leaders. Finally, we validate our theoretical results by a number of numerical
simulations on different clustered networks
Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels
In this paper we propose and analyze a distributed algorithm for achieving
globally optimal decisions, either estimation or detection, through a
self-synchronization mechanism among linearly coupled integrators initialized
with local measurements. We model the interaction among the nodes as a directed
graph with weights (possibly) dependent on the radio channels and we pose
special attention to the effect of the propagation delay occurring in the
exchange of data among sensors, as a function of the network geometry. We
derive necessary and sufficient conditions for the proposed system to reach a
consensus on globally optimal decision statistics. One of the major results
proved in this work is that a consensus is reached with exponential convergence
speed for any bounded delay condition if and only if the directed graph is
quasi-strongly connected. We provide a closed form expression for the global
consensus, showing that the effect of delays is, in general, the introduction
of a bias in the final decision. Finally, we exploit our closed form expression
to devise a double-step consensus mechanism able to provide an unbiased
estimate with minimum extra complexity, without the need to know or estimate
the channel parameters.Comment: To be published on IEEE Transactions on Signal Processin
On the convergence rate of distributed gradient methods for finite-sum optimization under communication delays
Motivated by applications in machine learning and statistics, we study
distributed optimization problems over a network of processors, where the goal
is to optimize a global objective composed of a sum of local functions. In
these problems, due to the large scale of the data sets, the data and
computation must be distributed over processors resulting in the need for
distributed algorithms. In this paper, we consider a popular distributed
gradient-based consensus algorithm, which only requires local computation and
communication. An important problem in this area is to analyze the convergence
rate of such algorithms in the presence of communication delays that are
inevitable in distributed systems. We prove the convergence of the
gradient-based consensus algorithm in the presence of uniform, but possibly
arbitrarily large, communication delays between the processors. Moreover, we
obtain an upper bound on the rate of convergence of the algorithm as a function
of the network size, topology, and the inter-processor communication delays
On Robustness Analysis of a Dynamic Average Consensus Algorithm to Communication Delay
This paper studies the robustness of a dynamic average consensus algorithm to
communication delay over strongly connected and weight-balanced (SCWB)
digraphs. Under delay-free communication, the algorithm of interest achieves a
practical asymptotic tracking of the dynamic average of the time-varying
agents' reference signals. For this algorithm, in both its continuous-time and
discrete-time implementations, we characterize the admissible communication
delay range and study the effect of the delay on the rate of convergence and
the tracking error bound. Our study also includes establishing a relationship
between the admissible delay bound and the maximum degree of the SCWB digraphs.
We also show that for delays in the admissible bound, for static signals the
algorithms achieve perfect tracking. Moreover, when the interaction topology is
a connected undirected graph, we show that the discrete-time implementation is
guaranteed to tolerate at least one step delay. Simulations demonstrate our
results
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
Asymptotic Consensus Without Self-Confidence
This paper studies asymptotic consensus in systems in which agents do not
necessarily have self-confidence, i.e., may disregard their own value during
execution of the update rule. We show that the prevalent hypothesis of
self-confidence in many convergence results can be replaced by the existence of
aperiodic cores. These are stable aperiodic subgraphs, which allow to virtually
store information about an agent's value distributedly in the network. Our
results are applicable to systems with message delays and memory loss.Comment: 13 page
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