Recursive state estimation for time-varying complex networks subject to missing measurements and stochastic inner coupling under random access protocol

Zhang, Hongxu; Hu, Jun; Liu, Hongjian; Yu, Xiaoyang; Liu, Fengqiu

doi:10.1016/j.neucom.2018.07.086

Cited by 90 publications

(47 citation statements)

References 35 publications

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“…At present the delays are generally divided into two categories according to the occurrence way, discrete delays and distributed delays. In the recent years, some works have been done on the analysis and synthesis of systems for various types of delays [7][8][9][10][11][12]. However, most of these studies considered one of the delays mentioned before, whereas few considered two kinds of delays simultaneously, that is, mixed delays (both discrete and distributed).…”

Section: Introductionmentioning

confidence: 99%

Finite-time $H_{\infty }$ bounded control of networked control systems with mixed delays and stochastic nonlinearities

Hou

Chen

2020

Adv Differ Equ

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In this study, we investigate the stochastic finite-time H ∞ bounded control problem for a class of networked control systems (NCSs) subject to mixed delays, stochastic nonlinearities, and randomly missing measurement. The mixed delays consist of discrete time-varying and distributed delays. The stochastic nonlinearities satisfy statistical means. The missing measurement is modeled by a Bernoulli-distributed random variable. By applying stochastic analysis method we present sufficient conditions guaranteeing the stochastic finite-time boundedness of a closed-loop system with desired H ∞ performance level within a finite time interval. Moreover, dynamic output feedback controller can be obtained in terms of a set of matrix inequalities, which can be easily solved by using the cone complementarity linearization method (CCLM). Finally, we provide two numerical examples to illustrate the validity of the proposed design technique.

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Section: Introductionmentioning

confidence: 99%

Finite-time $H_{\infty }$ bounded control of networked control systems with mixed delays and stochastic nonlinearities

Hou

Chen

2020

Adv Differ Equ

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“…The consensus has developed rapidly and yielded fruitful results, and has been widely applied to a variety of scientific and engineering problems, including synchronization of coupled oscillators, formation control, swarm control, optimal cooperative control, clustering, sensor networks, etc. (Lin, Zhang, & Liu, 2018;Zhang, Hu, Liu, Yu, & Liu, 2019). This paper will introduce mainly the development and research status from the following aspects: consensus subjected to communication constraints, leaderfollowing consensus, group consensus, consensus based on trigger mechanism, finite-time consensus, multiple consensus and multiple tracking.…”

Section: Introductionmentioning

confidence: 99%

A survey of the consensus for multi-agent systems

Tan

2019

Systems Science & Control Engineering

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Multi-agent systems (MASs) has developed into an emerging complex system science and gradually infiltrated into various fields of social life. The problem of consensus (i.e. all agents eventually to reach an agreement upon a common quantity of interest) is the basis of distributed coordinated control of the MASs, which has attracted tremendous attention from both theoretical and practical perspectives. This paper comprehensively reviews the state-of-the-art development in the consensus of MASs. Firstly, the basic framework and overview of MASs and consensus are discussed. Secondly, the motivations, results and methods of several kinds of consensus problems are introduced, including consensus subjected to communication constraints, leader-following consensus, group consensus, consensus based on trigger mechanism, finite-time consensus, multi-consensus and multi-tracking. Finally, some challenging issues and development trends of the consensus of MASs are considered.

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“…In the past two decades, the popularization of the Internet has greatly changed our way of life through the rapid communication ways [1][2][3]. Accordingly, many scholars have witnessed successful applications of recurrent neural networks (RNNs) in wide fields including pattern recognition, image processing, associate memory and optimization domains [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Variance-constrained resilient $H_{\infty }$ state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

Gao

Chen

et al. 2019

Adv Differ Equ

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This paper addresses the resilient H ∞ state estimation problem under variance constraint for discrete uncertain time-varying recurrent neural networks with randomly varying nonlinearities and missing measurements. The phenomena of missing measurements and randomly varying nonlinearities are described by introducing some Bernoulli distributed random variables, in which the occurrence probabilities are known a priori. Besides, the multiplicative noise is employed to characterize the estimator gain perturbation. Our main purpose is to design a time-varying state estimator such that, for all missing measurements, randomly varying nonlinearities and estimator gain perturbation, both the estimation error variance constraint and the prescribed H ∞ performance requirement are met simultaneously by providing some sufficient criteria. Finally, the feasibility of the proposed variance-constrained resilient H ∞ state estimation method is verified by some simulations.

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Recursive state estimation for time-varying complex networks subject to missing measurements and stochastic inner coupling under random access protocol

Cited by 90 publications

References 35 publications

Finite-time $H_{\infty }$ bounded control of networked control systems with mixed delays and stochastic nonlinearities

Finite-time $H_{\infty }$ bounded control of networked control systems with mixed delays and stochastic nonlinearities

A survey of the consensus for multi-agent systems

Variance-constrained resilient $H_{\infty }$ state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

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