Global synchronization in finite time for fractional-order coupling complex dynamical networks with discontinuous dynamic nodes

Jia, You; Wu, Huaiqin

doi:10.1016/j.neucom.2019.05.036

Cited by 16 publications

(9 citation statements)

References 48 publications

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“…By designing two state feedback controllers, several FTS criteria for such a model with fixed and adaptive couplings are established. An improved fractional‐order inequality

{D}^{\alpha }V(t)\le -\mu {V}^{\gamma }(t)

0<\gamma <1

is introduced to study FTS problems for fractional‐order systems. This improved inequality is a generalization of inequality in literatures 33,34 A novel adaptive scheme for fractional‐order systems to adjust the coupling weights is presented, which has ensured the MFCNNs can achieve FTS.…”

Section: Introductionmentioning

confidence: 95%

Finite‐time synchronization of multi‐weighted fractional‐order coupled neural networks with fixed and adaptive couplings

Tong

Chen

et al. 2022

Adaptive Control & Signal

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This article concentrates on the finite-time synchronization (FTS) for multi-weighted fractional-order coupled neural networks (MFCNNs) with fixed and adaptive couplings. A new dynamic model, which involves coupled neural networks with fractional order and multi-weighted couplings, is proposed. Furthermore, an adaptive law to adjust the coupling weights is devised to ensure the FTS of such a complex model. First, by using fractional-order calculus properties and inequality techniques, a finite-time fractional differential inequality is presented, which considerably extends the prior result. Second, by the Lyapunov stability theory and the fractional-order inequality, several sufficient conditions are acquired to make sure the FTS for MFCNNs. Moreover, the influence of the system's order on the FTS is also uncovered. Finally, a numerical simulation example and a practical simulation example are provided to demonstrate the validity of the obtained criteria.

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“…By designing two state feedback controllers, several FTS criteria for such a model with fixed and adaptive couplings are established. An improved fractional‐order inequality

{D}^{\alpha }V(t)\le -\mu {V}^{\gamma }(t)

0<\gamma <1

Section: Introductionmentioning

confidence: 95%

Finite‐time synchronization of multi‐weighted fractional‐order coupled neural networks with fixed and adaptive couplings

Tong

Chen

et al. 2022

Adaptive Control & Signal

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“…That is, the fractional dynamical networks can more precisely describe the characteristics of physical systems. Nowadays, many valuable results about the fractional-order networks have been obtained [18][19][20][21][22][23][24][25]. In [18], Si et al gave the sufficient condition of structure identification in uncertain fractional dynamical networks, and further discussed the effects of coupling strength and fractional order on the identification process.…”

Section: Introductionmentioning

confidence: 99%

“…It is worth noting that the networks in the aforementioned literature [18][19][20][21][22][23][24][25][26][27][28] are formed by many coupled subsystems with the same fractional order. In fact, complex networks may consist of subsystems with different fractional orders in the real world.…”

Section: Introductionmentioning

confidence: 99%

Structure Identification of Fractional-Order Dynamical Network with Different Orders

Zhou

2021

Mathematics

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Topology structure and system parameters have a great influence on the dynamical behavior of dynamical networks. However, they are sometimes unknown or uncertain in advance. How to effectively identify them has been investigated in various network models, from integer-order networks to fractional-order networks with the same order. In the real world, many systems consist of subsystems with different fractional orders. Therefore, the structure identification of a dynamical network with different fractional orders is investigated in this paper. Through designing proper adaptive controllers and parameter updating laws, two network estimators are well constructed. One is for identifying only the unknown topology structure. The other is for identifying both the unknown topology structure and system parameters. Based on the Lyapunov function method and the stability theory of fractional-order dynamical systems, the theoretical results are analytically proved. The effectiveness is verified by three numerical examples as well. In addition, the designed estimators have a good performance in monitoring switching topology. From the practical viewpoint, the designed estimators can be used to monitor the change of current and voltage in the fractional-order circuit systems.

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“…In recent years, many researchers have paid increasing attention to the studies of the complex dynamical networks (CDNs). Especially, synchronization problem of CDNs has received a lot of attention due to its potential applications (Jia and Wu, 2019;Kazemy and Shojaei, 2018;Liu et al, 2020c;Wang et al, 2019b;Wang et al, 2019c;Xia et al, 2019;Zhang et al, 2019a;Zhang et al, 2019c).…”

Section: Introductionmentioning

confidence: 99%

Pinning synchronization control for multi-layer complex networks via adaptive fuzzy logic system

Sun

Ren

2021

Transactions of the Institute of Measurement and Control

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In this paper, the pinning synchronization problem for the multi-layer networks with dynamic uncertainties is studied. The dynamical uncertainties can be approximated by a fuzzy logic system, based on which, the pinning synchronization scheme is proposed. By using Lyapunov stability theorem, the sufficient condition is given that can ensure that the multi-layer networks can synchronize to the reference trajectory with designed adaptive law. Finally, a numerical example is given to verify the effectiveness of the proposed pinning control scheme.

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Global synchronization in finite time for fractional-order coupling complex dynamical networks with discontinuous dynamic nodes

Cited by 16 publications

References 48 publications

Finite‐time synchronization of multi‐weighted fractional‐order coupled neural networks with fixed and adaptive couplings

Finite‐time synchronization of multi‐weighted fractional‐order coupled neural networks with fixed and adaptive couplings

Structure Identification of Fractional-Order Dynamical Network with Different Orders

Pinning synchronization control for multi-layer complex networks via adaptive fuzzy logic system

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