Stabilization and synchronization control for complex dynamical networks with dynamic link subsystem

Gao, Zilin; Liu, Lizhi; Wang, Yinhe; Gao, Peitao; Li, Yongfu

doi:10.1016/j.ins.2022.07.153

Cited by 14 publications

(5 citation statements)

References 40 publications

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“…Therefore, it is necessary to develop dynamic behaviors of CRDNNs. In model (5), the input u q (s, t) is added with k dimension to explore passivity property of CRDNNs, according to the definition of passivity, 50 which assumed that output and input vectors of network systems are different dimensions. Unfortunately, synchronization and passivity matters for directly CDRDNNs with different dimensional vectors via event-triggered pinning protocol has not been researched, which encourages us to develop the presented work.…”

Section: Network Depiction Of Directly Cdrdnnsmentioning

confidence: 99%

“…These massive applications heavily depend on dynamic properties of CNs, for example, stabilization and synchronization. So, numerous relevant results have been obtained 5‐9 . Sufficient conditions for network systems were derived to guarantee stabilization and synchronization 5 .…”

Section: Introductionmentioning

confidence: 99%

“…So, numerous relevant results have been obtained 5‐9 . Sufficient conditions for network systems were derived to guarantee stabilization and synchronization 5 . Reference 6 studied pinning synchronization matter for two kinds of CNs, and Reference 7 disposed of finite‐time synchronization with communication constraint.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Event‐triggered pinning control for passivity and synchronization of directly coupled delayed reaction‐diffusion neural networks

Lin,

Liu,

Huang

2023

Adaptive Control & Signal

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SummaryThis article solves event‐triggered pinning control for passivity and synchronization problems of directly coupled delayed reaction‐diffusion neural networks (CDRDNNs). Outer coupling matrix of the presented model can be direct, while previous literature referred to reaction‐diffusion neural networks usually assumed this matrix undirected simply. First, through exploiting appropriate event‐triggered pinning protocol, passivity and synchronization criteria are acquired for directly CDRDNNs on account of Lyapunov functional and inequality techniques. After that, we also deal with event‐triggered pinning passivity and synchronization for undirected CDRDNNs. In the end, the effectiveness of these obtained results is verified by given examples.

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Section: Network Depiction Of Directly Cdrdnnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Event‐triggered pinning control for passivity and synchronization of directly coupled delayed reaction‐diffusion neural networks

Lin,

Liu,

Huang

2023

Adaptive Control & Signal

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“…Complex dynamical networks (CDNs), comprised of a multitude of interconnected nodes, have garnered considerable attention in the realms of science and engineering [1]. In the field of network theory, CDNs can be regarded as an assembly of interconnected nodes, wherein each individual node symbolizes a dynamic system.…”

Section: Introductionmentioning

confidence: 99%

Sampled-Data Controller Design with Exponential Time-Varying Gain for Complex Dynamical Networks

Qian,

Mao,

Wan

et al. 2024

Frontiers in Artificial Intelligence and Applications

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The article is dedicated to the design of a sampled-data controller (SDC) with exponential time-varying gain (ETVG) for a kind of complex dynamical networks. It establishes a set of sufficient conditions, expressed as linear matrix inequalities, for the design of SDC with ETVG using coordinate conversion and Lyapunov function. Finally, the validity of the presented results is demonstrated through a practical example.

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“…Networks are prevalent in exploring the phenomenons and principles of our daily lives, e.g., traffic [ 1 , 2 ], financial [ 3 , 4 ], biological [ 5 – 8 ]and social systems [ 9 , 10 ]. As the ultimate goal regarding the exploration of these systems is to drive them to desired states, numerous advances have been achieved in the field of network control [ 11 – 16 ]. Controllability as the first step of control, quantifies whether a given system can be driven from any initial state to any desired state within finite time with finite external inputs.…”

Section: Introductionmentioning

confidence: 99%

Structural characteristics in network control of molecular multiplex networks

et al. 2023

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Numerous real-world systems can be naturally modeled as multilayer networks, providing an efficient tool to characterize these complex systems. Although recent progress in understanding the controlling of synthetic multiplex networks, how to control real multilayer systems remains poorly understood. Here, we explore the controllability and energy requirement of molecular multiplex networks coupled by transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network from the perspective of network structural characteristics. Our findings reveal that the driver nodes tend to avoid essential or pathogen-related genes. However, imposing external inputs on these essential or pathogen-related genes can remarkably reduce the energy cost, implying their crucial role in network control. Moreover, we find that the minimal driver nodes, as well as the energy required, are associated with disassortative coupling between TRN and PPI networks. Our results provide a comprehensive understanding of the roles of genes in biology and network control across several species.

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Stabilization and synchronization control for complex dynamical networks with dynamic link subsystem

Cited by 14 publications

References 40 publications

Event‐triggered pinning control for passivity and synchronization of directly coupled delayed reaction‐diffusion neural networks

Event‐triggered pinning control for passivity and synchronization of directly coupled delayed reaction‐diffusion neural networks

Sampled-Data Controller Design with Exponential Time-Varying Gain for Complex Dynamical Networks

Structural characteristics in network control of molecular multiplex networks

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