Finite‐Time lag synchronization of delayed neural networks via periodically intermittent control

Jing, Taiyan; Chen, Fangqi

doi:10.1002/cplx.21733

Cited by 9 publications

(9 citation statements)

References 42 publications

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“…The meaning of " " is the system state variable , the meaning of " " is the system state variable . Then the state variables in the observer (18) can be synchronized with the state variables in the system (17), and the unknown parameters , , , can be identified.…”

Section: Fixed-time Synchronization and Parameters Identificationmentioning

confidence: 99%

“…However, these methods cannot guarantee stability in a certain period of time and cannot guarantee convergence of speed error responses under uncertain parameters. Recently, the finite-time control and synchronization of the chaotic power system have attracted interests of many researchers [14][15][16][17][18]. In [19], the authors studied the chaos control of power system based on the finite-time stability theory.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Fixed-Time Stability Control and Parameters Identification for Chaotic Oscillation in Second Order Power System

Zhi-jie

et al. 2018

Mathematical Problems in Engineering

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In this paper, the novel adaptive fixed-time stability control for chaotic oscillation in second order power system is proposed. The settling time of fixed-time control can be adjusted to the desired value without knowing the initial condition, while the finite time control depends on that. Then, we develop a parameter identification method of fixed-time depending on synchronous observer with adaptive law of parameters, which can guarantee these uncertain parameters to be identified effectively. Finally, some numerical results demonstrate the effectiveness and practicability of the scheme.

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Section: Fixed-time Synchronization and Parameters Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Fixed-Time Stability Control and Parameters Identification for Chaotic Oscillation in Second Order Power System

Zhi-jie

et al. 2018

Mathematical Problems in Engineering

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“…Remark 3: When m = 2 and g(x) = Γx, (Γ denotes the inner-coupling matrix), the finite-time synchronization control for system (14) has been studied in [37]. Then, based on the same control strategy of [37], the finite-time lag synchronization for system (14) with m = 2 is studied in [43]. More similar systems to (14) have been studied in [29], [30], [38], [39], [40].…”

Section: F-      mentioning

confidence: 99%

Finite-time synchronization of multi-layer nonlinear coupled complex networks via intermittent feedback control

2017

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This paper addresses the problem of finite-time synchronization for a class of multi-layer nonlinear coupled complex networks via intermittent feedback control. Firstly, based on finite-time stability theory, some novel criteria are given to guarantee that the error system of drive-response systems is still finite-time stable under an inherently discontinuous controller. Then, by proposing two kinds of intermittent feedback control laws, sufficient conditions of finite-time synchronization of two kinds of multi-layer complex networks are derived, respectively. The time delay between different layers is also taken into consideration. Finally, a numerical example is provided to verify the effectiveness of the proposed methods. Index Terms Complex networks, finite-time synchronization, multi-layer, intermittent feedback control. I. I In the past few decades, the synchronization problem of complex networks has attracted more and more attention in practical applications [1], [2], [3], [4], [5], [6]. A basic complex network consists of some nodes and links between the nodes, where each node is a dynamic system. Since the problem of synchronization of chaotic systems has been studied in [1], synchronization as a potential engineering application has been applied into secure communication, neural network, biology and information processing [7], [8], [9], [10], [11]. Up till now, there are lots of different types of synchronization, for instance, complete synchronization [12], anti-synchronization [13], projective synchronization [14] and cluster synchronization [15], [16]. It should be noted that information of different nodes is transmitted based on a shared band-limited digital communication network. Thus, it is interesting to study synchronization of complex networks with delayed coupling. For example, global synchronization of a general linear coupled network has been studied with a time-varying coupling delay in [17]. Then, a developed generalized mixed outer synchronization are also studied with a time-varying coupling delay [18]. In [19], local and global synchronization of complex networks have been studied with a fixed delay. In [20], global exponential synchronization of

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“…Moreover, time delay is inevitable in real world due to limited speed of information processing or propagation, many researchers investigated FnTSyn via IC with time delay. For example, [27]- [29] used the following inequalities (Lemma 3 in [26]) V (t) ≤ −αV η (t), lT ≤ t < lT + θT, V (t) ≤ 0, t ∈ lT + θT ≤ t < (l + 1)T to investigate the FnTSyn via PIC, and the external controller in work time was composed of delay and also exist in rest time (see Eq. ( 5) in [27], Eq.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Finite Time Stability of Delayed Systems via Aperiodically Intermittent Control and Quantized Control

Liu,

2020

Preprint

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In this brief, we set up the finite time stability (FnTSta) theory for dynamical systems with bounded timevarying delays via aperiodically intermittent control (AIC) and quantized control (QC). A more general QC is designed in this brief. The bound of time-varying delay is required to be less than the infimum in AIC. Two-phases-method (2PM) is applied to solve the FnTSta for delayed system, i.e., it divides the whole proof process into two phases, one phase is that the process for the norm of system error evolving from initial values to 1, and the other is the process for the norm of system error evolving from 1 to 0. By proving that these two phases both use FnT to realize, the whole FnTSta for system is proved. We also design the adaptive rules and prove its validity rigorously. Furthermore, the obtained theories are used to discuss the finite time synchronization (FnTSyn) of neural networks as an application. Finally, some simulations are given to illustrate the effectiveness of our theoretical results.

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Finite‐Time lag synchronization of delayed neural networks via periodically intermittent control

Cited by 9 publications

References 42 publications

Adaptive Fixed-Time Stability Control and Parameters Identification for Chaotic Oscillation in Second Order Power System

Adaptive Fixed-Time Stability Control and Parameters Identification for Chaotic Oscillation in Second Order Power System

Finite-time synchronization of multi-layer nonlinear coupled complex networks via intermittent feedback control

Adaptive Finite Time Stability of Delayed Systems via Aperiodically Intermittent Control and Quantized Control

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