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Journal ArticleDOI

Sampled-Data Synchronization Control of Dynamical Networks With Stochastic Sampling

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TLDR
The addressed synchronization control problem is first formulated as an exponentially mean-square stabilization problem for a new class of dynamical networks that involve both the multiple probabilistic interval delays (MPIDs) and the sector-bounded nonlinearities (SBNs).
Abstract
This technical note is concerned with the sampled-data synchronization control problem for a class of dynamical networks. The sampling period considered here is assumed to be time-varying that switches between two different values in a random way with given probability. The addressed synchronization control problem is first formulated as an exponentially mean-square stabilization problem for a new class of dynamical networks that involve both the multiple probabilistic interval delays (MPIDs) and the sector-bounded nonlinearities (SBNs). Then, a novel Lyapunov functional is constructed to obtain sufficient conditions under which the dynamical network is exponentially mean-square stable. Both Gronwall's inequality and Jenson integral inequality are utilized to substantially simplify the derivation of the main results. Subsequently, a set of sampled-data synchronization controllers is designed in terms of the solution to certain matrix inequalities that can be solved effectively by using available software. Finally, a numerical simulation example is employed to show the effectiveness of the proposed sampled-data synchronization control scheme.

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Citations
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Distributed networked control systems

TL;DR: A brief overview of distributed networked control systems regarding system configurations, challenging issues and methodologies is presented and some potential research directions are suggested.
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Event-Triggering Sampling Based Leader-Following Consensus in Second-Order Multi-Agent Systems

TL;DR: The problem of second-order leader-following consensus by a novel distributed event-triggered sampling scheme in which agents exchange information via a limited communication medium is studied and it is shown that the inter-event intervals are lower bounded by a strictly positive constant, which excludes the Zeno-behavior before the consensus is achieved.
Journal ArticleDOI

Analysis and synthesis of networked control systems: A survey of recent advances and challenges.

TL;DR: This paper attempts to present an overview of recent advances and unify them in a framework of network-induced issues such as signal sampling, data quantization, communication delay, packet dropouts, medium access constraints, channel fading and power constraint, and present respective solution approaches to each of these issues.
Journal ArticleDOI

Reliable asynchronous sampled-data filtering of T–S fuzzy uncertain delayed neural networks with stochastic switched topologies

TL;DR: The intermittent fault-tolerance scheme is taken into fully account in designing a reliable asynchronous sampled-data controller, which ensures such that the resultant neural networks is asymptotically stable.
Journal ArticleDOI

Leader-Following Consensus of Nonlinear Multiagent Systems With Stochastic Sampling

TL;DR: It is shown that the leader-following consensus problem with stochastic sampling can be transferred into a master-slave synchronization problem with only one master system and two slave systems.
References
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Journal ArticleDOI

Statistical mechanics of complex networks

TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Book

Linear Matrix Inequalities in System and Control Theory

Edwin E. Yaz
TL;DR: In this paper, the authors present a brief history of LMIs in control theory and discuss some of the standard problems involved in LMIs, such as linear matrix inequalities, linear differential inequalities, and matrix problems with analytic solutions.
Journal ArticleDOI

Exploring complex networks

TL;DR: This work aims to understand how an enormous network of interacting dynamical systems — be they neurons, power stations or lasers — will behave collectively, given their individual dynamics and coupling architecture.
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