6,094 research outputs found
Performance analysis with network-enhanced complexities: On fading measurements, event-triggered mechanisms, and cyber attacks
Copyright © 2014 Derui Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Nowadays, the real-world systems are usually subject to various complexities such as parameter uncertainties, time-delays, and nonlinear disturbances. For networked systems, especially large-scale systems such as multiagent systems and systems over sensor networks, the complexities are inevitably enhanced in terms of their degrees or intensities because of the usage of the communication networks. Therefore, it would be interesting to (1) examine how this kind of network-enhanced complexities affects the control or filtering performance; and (2) develop some suitable approaches for controller/filter design problems. In this paper, we aim to survey some recent advances on the performance analysis and synthesis with three sorts of fashionable network-enhanced complexities, namely, fading measurements, event-triggered mechanisms, and attack behaviors of adversaries. First, these three kinds of complexities are introduced in detail according to their engineering backgrounds, dynamical characteristic, and modelling techniques. Then, the developments of the performance analysis and synthesis issues for various networked systems are systematically reviewed. Furthermore, some challenges are illustrated by using a thorough literature review and some possible future research directions are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 61203139, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Limitations and tradeoffs in synchronization of large-scale networks with uncertain links
We study synchronization in scalar nonlinear systems connected over a linear
network with stochastic uncertainty in their interactions. We provide a
sufficient condition for the synchronization of such network systems expressed
in terms of the parameters of the nonlinear scalar dynamics, the second and
largest eigenvalues of the mean interconnection Laplacian, and the variance of
the stochastic uncertainty. The sufficient condition is independent of network
size thereby making it attractive for verification of synchronization in a
large size network. The main contribution of this paper is to provide
analytical characterization for the interplay of roles played by the internal
dynamics of the nonlinear system, network topology, and uncertainty statistics
in network synchronization. We show there exist important tradeoffs between
these various network parameters necessary to achieve synchronization. We show
for nearest neighbor networks with stochastic uncertainty in interactions there
exists an optimal number of neighbors with maximum margin for synchronization.
This proves in the presence of interaction uncertainty, too many connections
among network components is just as harmful for synchronization as the lack of
connection. We provide an analytical formula for the optimal gain required to
achieve maximum synchronization margin thereby allowing us to compare various
complex network topology for their synchronization property
Time-and event-driven communication process for networked control systems: A survey
Copyright © 2014 Lei Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In recent years, theoretical and practical research topics on networked control systems (NCSs) have gained an increasing interest from many researchers in a variety of disciplines owing to the extensive applications of NCSs in practice. In particular, an urgent need has arisen to understand the effects of communication processes on system performances. Sampling and protocol are two fundamental aspects of a communication process which have attracted a great deal of research attention. Most research focus has been on the analysis and control of dynamical behaviors under certain sampling procedures and communication protocols. In this paper, we aim to survey some recent advances on the analysis and synthesis issues of NCSs with different sampling procedures (time-and event-driven sampling) and protocols (static and dynamic protocols). First, these sampling procedures and protocols are introduced in detail according to their engineering backgrounds as well as dynamic natures. Then, the developments of the stabilization, control, and filtering problems are systematically reviewed and discussed in great detail. Finally, we conclude the paper by outlining future research challenges for analysis and synthesis problems of NCSs with different communication processes.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
A Scalable Approach for Analysing Multi-Agent Systems with Heterogeneous Stochastic Packet Loss
An important aspect in jointly analysing networked control systems and their
communication is to model the networking in a sufficiently rich but at the same
time mathematically tractable way. As such, this paper improves on a recently
proposed scalable approach for analysing multi-agent systems with stochastic
packet loss by allowing for heterogeneous transmission probabilities and
temporal correlation in the communication model. The key idea is to consider
the transmission probabilities as uncertain, which facilitates the use of tools
from robust control. Due to being formulated in terms of linear matrix
inequalities that grow linearly with the number of agents, the result is
applicable to very large multi-agent systems, which is demonstrated by
numerical simulations with up to 10000 agents.Comment: 8 pages, 3 figure
Almost Sure Resilient Consensus Under Stochastic Interaction: Links Failure and Noisy Channels
The resilient consensus problem over a class
of discrete-time linear multiagent systems is addressed.
Because of external cyber-attacks, some agents are assumed
to be malicious and not following a desired cooperative
behavior. Thus, the objective consists in designing a
control strategy for the healthy agents to reach consensus
upon their state vectors, while due to interaction among the
agents, the malicious agents try to prevent them to achieve
consensus. Although this problem has been investigated
by some researchers, under the existing approaches in the
literature, achieving consensus is only guaranteed when
the information exchange among the agents is deterministic.
Based on this motivation, the main contribution of
the paper is on almost sure resilient consensus control of
a network of healthy agents in the presence of stochastic
links failure and communication noises. We design a
discrete-time protocol for the set of the healthy agents, and
we show that under some probabilistic conditions on interaction
among the agents, achieving almost sure consensus
among the healthy agents can be guaranteed. The results
also are verified by numerical examples
An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination
This article reviews some main results and progress in distributed
multi-agent coordination, focusing on papers published in major control systems
and robotics journals since 2006. Distributed coordination of multiple
vehicles, including unmanned aerial vehicles, unmanned ground vehicles and
unmanned underwater vehicles, has been a very active research subject studied
extensively by the systems and control community. The recent results in this
area are categorized into several directions, such as consensus, formation
control, optimization, task assignment, and estimation. After the review, a
short discussion section is included to summarize the existing research and to
propose several promising research directions along with some open problems
that are deemed important for further investigations
-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations
The paper considers a class of multi-agent Markov decision processes (MDPs),
in which the network agents respond differently (as manifested by the
instantaneous one-stage random costs) to a global controlled state and the
control actions of a remote controller. The paper investigates a distributed
reinforcement learning setup with no prior information on the global state
transition and local agent cost statistics. Specifically, with the agents'
objective consisting of minimizing a network-averaged infinite horizon
discounted cost, the paper proposes a distributed version of -learning,
-learning, in which the network agents collaborate by means of
local processing and mutual information exchange over a sparse (possibly
stochastic) communication network to achieve the network goal. Under the
assumption that each agent is only aware of its local online cost data and the
inter-agent communication network is \emph{weakly} connected, the proposed
distributed scheme is almost surely (a.s.) shown to yield asymptotically the
desired value function and the optimal stationary control policy at each
network agent. The analytical techniques developed in the paper to address the
mixed time-scale stochastic dynamics of the \emph{consensus + innovations}
form, which arise as a result of the proposed interactive distributed scheme,
are of independent interest.Comment: Submitted to the IEEE Transactions on Signal Processing, 33 page
Robust Performance Analysis for Time-Varying Multi-Agent Systems with Stochastic Packet Loss
Recently, a scalable approach to system analysis and controller synthesis for
homogeneous multi-agent systems with Bernoulli distributed packet loss has been
proposed. As a key result of that line of work, it was shown how to obtain
upper bounds on the -norm that are robust with respect to uncertain
interconnection topologies. The main contribution of the current paper is to
show that the same upper bounds hold not only for uncertain but also
time-varying topologies that are superimposed with the stochastic packet loss.
Because the results are formulated in terms of linear matrix inequalities that
are independent of the number of agents, multi-agent systems of any size can be
analysed efficiently. The applicability of the approach is demonstrated on a
numerical first-order consensus example, on which the obtained upper bounds are
compared to estimates from Monte-Carlo simulations.Comment: 8 pages, 4 figures. Extended version of a paper to be published at
IFAC World Congress 202
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