Synchronization in Networks of Linearly Coupled Dynamical Systems via Event-Triggered Diffusions

Lu, Wenlian; Han, Yujuan; Chen, Tianping

doi:10.1109/tnnls.2015.2402691

Cited by 67 publications

(26 citation statements)

References 23 publications

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“…where i = 0, 1 and k ∈ N in j . The exact form of (µ i0 ) k and (σ 2 i0 ) k are given in (20), (21), (22) and (23), and the test statistics T k is defined in Sub-section VIII-A. f P DF (T k |H i ) is the probability density function (PDF) of test statistics under each hypothesis H i , i = 0, 1.…”

Section: B a Learning-based Distributed Algorithm For Mitigating Thementioning

confidence: 99%

“…In order to estimate the parameters in the set θ 0 for the case of honest agents under the hypotheses H 0 and H 1 , we can simply utilize the method of moments [47]. For the honest neighbors, we know that the data should preferably follow a normal distribution with the means and variences given in (20), (21), (22) and (23). We assume the learning iterations of length L p (each learning iteration consists of L p data points).…”

Section: B a Learning-based Distributed Algorithm For Mitigating Thementioning

confidence: 99%

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Learning-Based Event-Triggered Control for Synchronization of Passive Multiagent Systems Under Attack

Rahnama

Antsaklis

2020

IEEE Trans. Automat. Contr.

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In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required communication load on the band-limited network. We define a general Byzantine attack on the event-triggered multi-agent network system and characterize its negative effects on synchronization. The Byzantine agents are capable of intelligently falsifying their data and manipulating the underlying communication graph by altering their respective control feedback weights. We introduce a decentralized detection framework and analyze its steady-state and transient performances. We propose a way of identifying individual Byzantine neighbors and a learning-based method of estimating the attack parameters. Lastly, we propose learning-based control approaches to mitigate the negative effects of the adversarial attack. I. INTRODUCTIONDistributed coordination of multi-agent systems has been discussed extensively in control, communication and computer science literature. The wide range of applications in this area includes multiple robot coordination [1], cooperative control of vehicle formations [2], flocking [3] and spacecraft formation flying [4]. A strong body of literature exists on the state synchronization of homogeneous multi-agent systems with identical dynamics. In many practical Arash Rahnama and Panos J. Antsaklis are with the communication graph is usually required to meet certain conditions for synchronization to happen[11]- [13]. Lastly, we propose a distributed method of detection and mitigation as opposed to the more common centralized methods where a fusion center takes upon itself the responsibility of detecting and mitigating the attacks. There is obviously always a limitation to this approach as the central fusion unit may be compromised as well. Our proposed distributed detection and mitigation framework will eliminate this possibility. In the consensus literature, the decentralized method of detection has been proposed in works such as [30]- [33]. In [33] for example, it is assumed that through collaboration, the Byzantine agents are aware of the true hypothesis, which is similar to the assumption we make in the present work. As another example, in [32], the authors rely on a sequential decentralize probability ratio test that is modified via a reputation-based mechanism in order to filter out the false data and only accept reliable messages. Lastly, most detection and mitigation frameworks in the literature rely on exclusion of Byzantine agents from the synchronization algorithm [34], [35]. For example, in [36], the authors propose an adaptive outlier detection framework, based on which, the outside of the bound received information are excluded from the consensus process. In our work, we propose a mitigation scheme that takes advantage of the falsified information received from the Byzantin...

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Section: B a Learning-based Distributed Algorithm For Mitigating Thementioning

confidence: 99%

Section: B a Learning-based Distributed Algorithm For Mitigating Thementioning

confidence: 99%

Learning-Based Event-Triggered Control for Synchronization of Passive Multiagent Systems Under Attack

Rahnama

Antsaklis

2020

IEEE Trans. Automat. Contr.

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“…As we know, [19] firstly introduced the triggering event in stabilizing control tasks, and event-based agreement protocols were used for multi-agent networks with in-depth study [20]. So far, numerous synchronization results of CNs with event-triggered mechanism have been reported [21][22][23][24][25]. In [22], the synchronization problem of CNs was handled by exploiting two types of triggering-event laws.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered H∞$\mathcal {H}_\infty$ synchronization of directed and switched coupled stochastic delayed neural networks with multi‐weights

Lin

Liu

Huang

2022

IET Control Theory & Appl

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This essay is devoted to studying scriptH∞$\mathcal {H}_\infty$ synchronization problem of directed and switched coupled stochastic delayed neural networks with multi‐weights (SCSDNNMWs) via designing a novel event‐triggered protocol. The proposed model is based on stochastic delayed neural network with directed coupling, and multiple coupling matrices with switching express intricate and changing communication channels. Since the external disturbances are likely to cause network topologies change, fixed and accurate values of coupling matrices are difficult to obtain, while the above model has more advantages of characterizing this real network. Besides, zero eigenvalue of different coupling matrices are represented by the corresponding normalized left eigenvectors (NLEVec), which is hard to establish yet especially when designing necessary Lyapunov functions. The authors' essay deduces that if the Chebyshev distance among NLEVec of multiple coupling matrices is less than a permissible deviation limit based on a weighted set of these NLEVec, then several event‐triggered scriptH∞$\mathcal {H}_\infty$ synchronization and synchronization criteria are established. The effectiveness of these results are verified by several given examples.

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“…Recently, achievements related to ETC have been achieved, for example, [26‐35]. Moreover, the synchronization schemes via ETC have also been proposed in [14,37‐45]. But there are very few results on synchronization via event‐triggered PIC and APIC.…”

Section: Introductionmentioning

confidence: 99%

Uniform synchronization for chaotic systems via event‐triggered aperiodic intermittent control

Yang

Liu

2020

Asian Journal of Control

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This paper studies the uniform synchronization for chaotic systems via event‐triggered aperiodic intermittent control (E‐APIC). A synchronization scheme via E‐APIC is proposed for master‐slave chaotic systems. Based on the error state and three indices of threshold‐value, check‐period, and control‐free index, E‐APIC is designed and the synchronization is achieved by such an E‐APIC. Also, a simplified E‐APIC is derived by letting the control‐free index trend to zero. With respect to the uncertainties, the simplified E‐APIC is strengthened to be robust for the uniform synchronization. Moreover, in order to compare with the synchronization schemes via periodic intermittent control (PIC), time‐based aperiodic intermittent control (T‐APIC), and event‐triggered impulsive control (ETIC), criteria of synchronization via PIC and T‐APIC and the scheme of ETIC are given respectively. And by using the same control width, the theoretical results on rate of control (RoC) and number of control (NoC) are derived respectively and show that E‐APIC has less RoC and NoC than both PIC and T‐APIC. The chaotic Chua's system is used to test the uniform synchronization via PIC, T‐APIC, E‐APIC, and ETIC. The numerical simulations show that E‐APIC has better performances on aspects of NoC, RoC, cost, and average non‐control width than PIC, T‐APIC, and ETIC.

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Synchronization in Networks of Linearly Coupled Dynamical Systems via Event-Triggered Diffusions

Cited by 67 publications

References 23 publications

Learning-Based Event-Triggered Control for Synchronization of Passive Multiagent Systems Under Attack

Learning-Based Event-Triggered Control for Synchronization of Passive Multiagent Systems Under Attack

Event‐triggered H∞$\mathcal {H}_\infty$ synchronization of directed and switched coupled stochastic delayed neural networks with multi‐weights

Uniform synchronization for chaotic systems via event‐triggered aperiodic intermittent control

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