10 research outputs found
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation
We study distributed average consensus problems in multi-agent systems with
directed communication links that are subject to quantized information flow.
The goal of distributed average consensus is for the nodes, each associated
with some initial value, to obtain the average (or some value close to the
average) of these initial values. In this paper, we present and analyze a
distributed averaging algorithm which operates exclusively with quantized
values (specifically, the information stored, processed and exchanged between
neighboring agents is subject to deterministic uniform quantization) and relies
on event-driven updates (e.g., to reduce energy consumption, communication
bandwidth, network congestion, and/or processor usage). We characterize the
properties of the proposed distributed averaging protocol on quantized values
and show that its execution, on any time-invariant and strongly connected
digraph, will allow all agents to reach, in finite time, a common consensus
value represented as the ratio of two integer that is equal to the exact
average. We conclude with examples that illustrate the operation, performance,
and potential advantages of the proposed algorithm
Event-triggered Consensus for Multi-agent Systems with Asymmetric and Reducible Topologies
This paper studies the consensus problem of multi-agent systems with
asymmetric and reducible topologies. Centralized event-triggered rules are
provided so as to reduce the frequency of system's updating. The diffusion
coupling feedbacks of each agent are based on the latest observations from its
in-neighbors and the system's next observation time is triggered by a criterion
based on all agents' information. The scenario of continuous monitoring is
first considered, namely all agents' instantaneous states can be observed. It
is proved that if the network topology has a spanning tree, then the
centralized event-triggered coupling strategy can realize consensus for the
multi-agent system. Then the results are extended to discontinuous monitoring,
where the system computes its next triggering time in advance without having to
observe all agents' states continuously. Examples with numerical simulation are
provided to show the effectiveness of the theoretical results
Pull-Based Distributed Event-triggered Consensus for Multi-agent Systems with Directed Topologies
This paper mainly investigates consensus problem with pull-based
event-triggered feedback control. For each agent, the diffusion coupling
feedbacks are based on the states of its in-neighbors at its latest triggering
time and the next triggering time of this agent is determined by its
in-neighbors' information as well. The general directed topologies, including
irreducible and reducible cases, are investigated. The scenario of distributed
continuous monitoring is considered firstly, namely each agent can observe its
in-neighbors' continuous states. It is proved that if the network topology has
a spanning tree, then the event-triggered coupling strategy can realize
consensus for the multi-agent system. Then the results are extended to
discontinuous monitoring, i.e., self-triggered control, where each agent
computes its next triggering time in advance without having to observe the
system's states continuously. The effectiveness of the theoretical results are
illustrated by a numerical example finally.Comment: arXiv admin note: text overlap with arXiv:1407.137