90 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
A Privacy-Preserving Finite-Time Push-Sum based Gradient Method for Distributed Optimization over Digraphs
This paper addresses the problem of distributed optimization, where a network
of agents represented as a directed graph (digraph) aims to collaboratively
minimize the sum of their individual cost functions. Existing approaches for
distributed optimization over digraphs, such as Push-Pull, require agents to
exchange explicit state values with their neighbors in order to reach an
optimal solution. However, this can result in the disclosure of sensitive and
private information. To overcome this issue, we propose a
state-decomposition-based privacy-preserving finite-time push-sum (PrFTPS)
algorithm without any global information such as network size or graph
diameter. Then, based on PrFTPS, we design a gradient descent algorithm
(PrFTPS-GD) to solve the distributed optimization problem. It is proved that
under PrFTPS-GD, the privacy of each agent is preserved and the linear
convergence rate related to the optimization iteration number is achieved.
Finally, numerical simulations are provided to illustrate the effectiveness of
the proposed approach
Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems
In this paper, a Hankel matrix-based fully distributed algorithm is proposed
to address a minimal-time deadbeat consensus prediction problem for
discrete-time high-order multi-agent systems (MASs). Therein, each agent can
predict the consensus value with the minimum number of observable historical
outputs of its own. Accordingly, compared to most existing algorithms only
yielding asymptotic convergence, the present method can attain deadbeat
consensus instead. Moreover, based on the consensus value prediction, instant
individual disagreement degree value of MASs can be calculated in advance as
well. Sufficient conditions are derived to guarantee both the minimal-time
deadbeat consensus and the instant individual disagreement degree prediction.
Finally, both the effectiveness and superiority of the proposed deadbeat
consensus algorithm are substantiated by numerical simulations.Comment: 12 pages, 3 figure
Event-Triggered Algorithms for Leader-Follower Consensus of Networked Euler-Lagrange Agents
This paper proposes three different distributed event-triggered control
algorithms to achieve leader-follower consensus for a network of Euler-Lagrange
agents. We firstly propose two model-independent algorithms for a subclass of
Euler-Lagrange agents without the vector of gravitational potential forces. By
model-independent, we mean that each agent can execute its algorithm with no
knowledge of the agent self-dynamics. A variable-gain algorithm is employed
when the sensing graph is undirected; algorithm parameters are selected in a
fully distributed manner with much greater flexibility compared to all previous
work concerning event-triggered consensus problems. When the sensing graph is
directed, a constant-gain algorithm is employed. The control gains must be
centrally designed to exceed several lower bounding inequalities which require
limited knowledge of bounds on the matrices describing the agent dynamics,
bounds on network topology information and bounds on the initial conditions.
When the Euler-Lagrange agents have dynamics which include the vector of
gravitational potential forces, an adaptive algorithm is proposed which
requires more information about the agent dynamics but can estimate uncertain
agent parameters.
For each algorithm, a trigger function is proposed to govern the event update
times. At each event, the controller is updated, which ensures that the control
input is piecewise constant and saves energy resources. We analyse each
controllers and trigger function and exclude Zeno behaviour. Extensive
simulations show 1) the advantages of our proposed trigger function as compared
to those in existing literature, and 2) the effectiveness of our proposed
controllers.Comment: Extended manuscript of journal submission, containing omitted proofs
and simulation
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