Fixed/preassigned-time bipartite synchronization for delayed fractional-order multilayer multi-link signed networks via adaptive aperiodically semi-intermittent control

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qiu Peng, Xiaotang Zhang, Manchun Tan
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引用次数: 0

Abstract

In this paper, the bipartite synchronization (BS) problem of fractional-order multilayer multi-link signed networks (FOMMKSNs) with internal time-varying delay, intra-layer and inter-layer coupling time-varying delays is studied in fixed-time or preassigned-time. Firstly, a mathematical model is established by integrating the fractional-order dynamics of nodes, the edges of multilayer and multi-link networks, and the positive and negative weights, making it more diverse and practical. Secondly, an expanded fixed-time aperiodically intermittent strategy theory is proposed, building on the existing theory, along with an improved estimation of the settling time (ST). Then, utilizing the signed graph theory and Lyapunov method, several sufficient conditions for fixed-time BS (FXTBS) of FOMMKSNs are derived through an adaptive aperiodically semi-intermittent control scheme. Furthermore, based on the FXTBS results, a new controller is designed to study the BS of FOMMKSNs within a specified time, where the ST is independent of any initial values and parameters of the network and the controller. Finally, the effectiveness of the obtained results is verified by two simulation examples.
基于自适应非周期半间歇控制的延迟分数阶多层多链路签名网络的固定/预分配时间二部同步
研究了具有内变时延、层内、层间耦合时变时延的分数阶多层多链路签名网络(formmksns)在固定时间或预分配时间下的二部同步问题。首先,将节点的分数阶动态、多层多链路网络的边缘以及正负权值相结合,建立数学模型,使其更具多样性和实用性;其次,在现有理论的基础上,提出了一个扩展的固定时间非周期性间歇策略理论,并改进了稳定时间(ST)的估计。然后,利用符号图理论和Lyapunov方法,通过一种自适应非周期半间歇控制方案,得到了formmksns的固定时间BS (FXTBS)的几个充分条件。此外,基于FXTBS结果,设计了一种新的控制器来研究FOMMKSNs在指定时间内的BS,其中ST与网络和控制器的任何初始值和参数无关。最后,通过两个仿真算例验证了所得结果的有效性。
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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