Adaptive fuzzy control for quasi-synchronization of uncertain complex dynamical networks with time-varying topology via event-triggered communication strategy

Hu, Taotao; He, Zheng; Zhang, Xiaojun; Zhong, Shouming; Shi, Kaibo; Zhang, Yaoyi

doi:10.1016/j.ins.2021.10.036

Cited by 22 publications

(7 citation statements)

References 42 publications

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“…Lemma 8 (see [40] Theorem 9. If Assumptions 2-5 are satisfied, the drive system (1) and the response system (2) can realize function projective synchronization with the controller (7) and adaptive laws (8)- (12).…”

Section: Controller Designmentioning

confidence: 99%

“…Among many researches on complex networks, synchronization research is one of the most important branches. So far, many types of synchronization have been investigated, such as complete synchronization [1,2], antisynchronization [3], exponential synchronization [4][5][6], quasisynchronization [7], lag synchronization [8,9], combined synchronization [10], projection synchronization [11], and function projection synchronization [12][13][14]. Function projection synchronization is a general synchronization form, which means that the driving system and the response system can be synchronized according to a certain function proportional relationship.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Function Projective Synchronization of Two Complex Networks with Unknown Sector Nonlinear Input and Multiple Time-Varying Delay Couplings

Fang

Wei

Yin

et al. 2022

Advances in Mathematical Physics

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This paper deals with the function projective synchronization of two complex dynamic networks with unknown sector nonlinear input, multiple time-varying delay couplings, model uncertainty, and external interferences. Based on Lyapunov stability theory and inequality transformation method, the robust adaptive synchronization controller is designed, by which the drive and response systems can achieve synchronization according to the function scaling factor. Different from some existing studies on nonlinear system with sector nonlinear input, this paper studies the synchronization of two complex dynamic networks when the boundary of sector nonlinear input is unknown. The controller does not include the boundary value of the sector nonlinear input and the time delay term, so it is more practical and relatively easy to implement. The corresponding simulation examples demonstrate the effectiveness of the proposed scheme.

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Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Function Projective Synchronization of Two Complex Networks with Unknown Sector Nonlinear Input and Multiple Time-Varying Delay Couplings

Fang

Wei

Yin

et al. 2022

Advances in Mathematical Physics

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“…Hu et al 13 studied the quasi-synchronization problem of uncertain complex networks based on event-triggered communication strategies in time-varying topology, and proposed a periodic time-varying network topology applicable to CDNS (Uncertain Complex Dynamical Networks) . Wang et al 14 solved the synchronization problem of nonlinear coupled complex networks through event-dependent aperiodic intermittent fixed control, and established an event-dependent intermittent control strategy based on region division.…”

Section: Introductionmentioning

confidence: 99%

Study of workshop network stability based on pinning control in disturbance environment

Cui

et al. 2023

Sci Rep

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In the production process, the manufacturing behavior and all the essential factors are affected by several disturbance factors, showing a complex dynamic fluctuation law. It makes the stability control process a difficult problem in environmental constraints. In this paper, the workshop production process is considered, and an improved coupled map lattice workshop production network state model is proposed. On this basis, the controller with the function of resource load protection is designed, and the network state model of the workshop based on the pinning control is developed. Three kinds of stability control strategies, SAC (Self-adaption Control) , SC (Self-acting Control) and PC (Pinning Control) , are designed based on disturbance triggering behavior and node state transition rules. In addition, two control effect evaluation indexes, RTS (Recovery Time Steps) and NFT (Node Failure Times) are designed. Considering the actual production data of diesel fuel injection system parts production workshop as example, the model is simulated and verified. The results show that under different disturbance intensities, compared with the SAC strategy, the RTS-Average value of the PC strategy is reduced by 29.83% on average, and the NFT-Average values are reduced by 46.9% on average. This proves that the pinning control strategy has certain advantages in controlling time length and propagation scale of disturbance propagation.

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“…In Li et al (2022), two event-triggered impulsive control methods were proposed, and sufficient criteria were formulated to guarantee the globally exponential stability of impulsive systems. Till now, several effective event-triggering schemes for quasi-synchronization have been investigated by Zhou and Zeng (2019), Yan et al (2020), and Hu et al (2022). In Hu et al (2022), for quasi-synchronization, an event-triggered communication mechanism under the switching topology was designed for complex NNs.…”

Section: Introductionmentioning

confidence: 99%

“…Till now, several effective event-triggering schemes for quasi-synchronization have been investigated by Zhou and Zeng (2019), Yan et al (2020), and Hu et al (2022). In Hu et al (2022), for quasi-synchronization, an event-triggered communication mechanism under the switching topology was designed for complex NNs. Then, the hybrid event-triggered scheme was investigated to solve the quasisynchronization problem for delayed MNNs with a novel threshold function (Zhou and Zeng, 2019;Yan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect

Yuan

Luo

et al. 2022

Front. Neurorobot.

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The dynamic behavior of memristive neural networks (MNNs), including synchronization, effectively keeps the robotic stability against numerous uncertainties from the mimic of the human brain. However, it is challenging to perform projective quasi-synchronization of coupled MNNs with low-consumer control devices. This is partly because complete synchronization is difficult to realize under various projective factors and parameter mismatch. This article aims to investigate projective quasi-synchronization from the perspective of the controller. Here, two approaches are considered to find the event-triggered scheme for lag synchronization of coupled MNNs. In the first approach, the projective quasi-synchronization issue is formulated for coupled MNNs for the first time, where the networks are combined with time-varying delays and uncertainties under the constraints imposed by the frequency of controller updates within limited system communication resources. It is shown that our methods can avoid the Zeno-behavior under the newly determined triggered functions. In the second approach, following classical methods, a novel projective quasi-synchronization criterion that combines the nonlinear property of the memristor and the framework of Lyapunov-Krasovskii functional (LKF) is proposed. Simulation results indicate that the proposed two approaches are useful for coupled MNNs, and they have less control cost for different types of quasi-synchronization.

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Adaptive fuzzy control for quasi-synchronization of uncertain complex dynamical networks with time-varying topology via event-triggered communication strategy

Cited by 22 publications

References 42 publications

Function Projective Synchronization of Two Complex Networks with Unknown Sector Nonlinear Input and Multiple Time-Varying Delay Couplings

Function Projective Synchronization of Two Complex Networks with Unknown Sector Nonlinear Input and Multiple Time-Varying Delay Couplings

Study of workshop network stability based on pinning control in disturbance environment

Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect

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