Event-triggered hybrid impulsive control for synchronization of memristive neural networks

Zhang, Yijun; Bao, Yuangui

doi:10.1007/s11432-019-2694-y

Cited by 20 publications

(5 citation statements)

References 45 publications

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“…As a significant direction, the cooperative control of MASs is widely studied. The cooperative behaviors can be divided into consensus, 6,7 formation 8 and synchronization, 9 among which consensus control is the basic problem of cooperative control. And the research results in consensus control have been extensively utilized in numerous specific and practical areas, such as multi‐manipulator systems, 10 wireless sensor networks, unmanned aerial vehicles, 11 and so on.…”

Section: Introductionmentioning

confidence: 99%

Adaptive impulsive security consensus control of uncertain multi‐agent systems with randomly occurring nonlinearities

Ren

Chen

2022

Intl J Robust & Nonlinear

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In practice, deception attacks, uncertainties, and randomly occurring nonlinearities (RONs) in multi-agent systems (MASs) are ubiquitous. Consequently, this paper settles the problem of secure consensus tracking control for uncertain MASs with randomly occurring nonlinearities under sensor attacks by adopting adaptive impulsive control methods. Compared with the previous works, a class of state-dependent sensor attack is firstly considered in the impulsive control. Meanwhile, a novel adaptive algorithm is introduced to deal with unknown system matrices, randomly occurring nonlinearities and uncertain parameters caused by sensor attacks. Sufficient conditions are derived to achieve leader-following consensus for the MASs by adaptive impulsive control under sensor attacks. Lastly, two simulation examples illustrate the validity of theorems in this paper.

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Section: Introductionmentioning

confidence: 99%

Adaptive impulsive security consensus control of uncertain multi‐agent systems with randomly occurring nonlinearities

Ren

Chen

2022

Intl J Robust & Nonlinear

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“…Du et al [33] introduced the impulsive control and ET scheme into differential evolution, aiming at increasing the search efficiency and the diversity of the population by instantly letting selected individuals move close to the desired positions. In addition, this strategy can also synchronise the non-linear delay systems [34], memristive delay NNs [35,36], disconnected switching topology multiple delay NNs [37] and so on. However, the impulsive controller in [32][33][34][35][36][37] did not consider the delay term, in which the instantaneous impulsive jump only depends on the current state.…”

Section: Introductionmentioning

confidence: 99%

“…Very recently, ET impulsive control strategy has been becoming an emergent hot topic about synchronisation of multisystem networks (see [32][33][34][35][36][37][38] and references therein), which inherit the respective merits of ET control and impulsive control. Comparing with general impulsive control, the ET impulsive strategy is more flexible as the impulse instants are determined by ET condition.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, this strategy can also synchronise the non-linear delay systems [34], memristive delay NNs [35,36], disconnected switching topology multiple delay NNs [37] and so on. However, the impulsive controller in [32][33][34][35][36][37] did not consider the delay term, in which the instantaneous impulsive jump only depends on the current state. In fact, packet loss sometimes occurs in the system networks due to packet collision caused by frequent data sampling.…”

Section: Introductionmentioning

confidence: 99%

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Synchronisation of multiple neural networks via event‐triggered time‐varying delay hybrid impulsive control

Ruan

Feng

et al. 2021

IET Control Theory & Appl

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This article discusses the exponential synchronisation problem for multiple neural networks with time-varying delay and a more general non-Laplacian coupling matrix is considered. To reduce the redundant communication, two event-based time-varying delay hybrid impulsive control schemes are proposed with static event-triggered condition and dynamic event-triggered condition. These novel control schemes neatly combine the eventtriggered coupling strategy with the time-varying delay hybrid impulsive control, where the impulsive instant is only determined by the specified event-triggered condition. Moreover, a delay impulsive differential inequality is established to discuss the synchronisation problem for controlled multiple neural networks. By using Stolz's theorem and introducing the average event-triggered delay impulsive gain, some less conservative sufficient conditions are derived to assure the stability of the time-varying delay multiple neural networks. Furthermore, the Zeno behaviour can be eliminated effectively and the consumption of the computing resources can be reduced. Finally, some examples are given to verify the superiority of the results. First, a static event-triggered time-varying delay hybrid impulsive control scheme was proposed to lessen the communication burden. Then, a time-varying delay impulsive differential inequality was established to guarantee the synchronisation of multiple neural networks. Moreover, the authors also discussed the issue that continuously triggering and sampling can be excluded.

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“…Its aim is to design a controller such that all states or outputs of agents in multiagent systems converge to a common value. Some published results on the synchronization have been displayed in References 1‐11, just to name a few. These results were obtained based on a common hypothesis that there exists a safe data exchange between agents via a shared network.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered output synchronization for nonhomogeneous agent systems with periodic denial‐of‐service attacks

Fang

Pan

et al. 2020

Intl J Robust & Nonlinear

129

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Summary This article presents a two‐layered framework to investigate the output synchronization problem in nonhomogeneous agent systems in the presence of periodic energy‐limited denial‐of‐service (DoS) attacks with an event‐triggered control strategy. Under the developed framework, the proposed controller for different agents is composed of a dynamic compensator and a regulator, where the dynamic compensator on the first layer can copy the dynamics of the leader node. DoS attacks with a periodic (partially known) attack strategy are based on a time sequence and interrupt the data exchange over a shard network consisting of the leader node and dynamic compensators in the first layer. An event‐triggered control protocol is proposed to reduce the update frequency of the controller, and it also ensures that the output of every dynamic compensator can track the output of the leader node without occurring “Zeno phenomenon.” Based on the output regulator theory, the regulator for every agent can guarantee that all nonhomogeneous agents can converge to the trajectory of every dynamic compensator in the bottom layer. Finally, a simulation example is demonstrated to verify the new design method developed.

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Event-triggered hybrid impulsive control for synchronization of memristive neural networks

Cited by 20 publications

References 45 publications

Adaptive impulsive security consensus control of uncertain multi‐agent systems with randomly occurring nonlinearities

Adaptive impulsive security consensus control of uncertain multi‐agent systems with randomly occurring nonlinearities

Synchronisation of multiple neural networks via event‐triggered time‐varying delay hybrid impulsive control

Event‐triggered output synchronization for nonhomogeneous agent systems with periodic denial‐of‐service attacks

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