Distributed dynamic event-triggered control for fixed/preassigned-time output synchronization of output-coupling complex networks

被引:11
作者
Feng, Liang [1 ]
Hu, Cheng [1 ]
Zhu, Quanxin [2 ]
Kong, Fanchao [3 ]
Wen, Shiping [4 ]
机构
[1] Xinjiang Univ, Coll Math & Syst Sci, Urumqi 830017, Xinjiang, Peoples R China
[2] Hunan Normal Univ, Sch Math & Stat, Changsha 410081, Peoples R China
[3] Anhui Normal Univ, Sch Math & Stat, Wuhu 241000, Anhui, Peoples R China
[4] Univ Technol Sydney, Ctr Artificial Intelligence, Ultimate 2007, Australia
基金
中国国家自然科学基金;
关键词
Homogeneous complex networks; Output coupling; Dynamic event-triggered control; Fixed-time synchronization; Preassigned-time synchronization; LEADERLESS CONSENSUS; SYSTEMS;
D O I
10.1016/j.ins.2023.119651
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The inaccessibility of the state information caused by irresistible factors is quite common in complex networks (CNs) and the synchronization is usually expected to be realized in a finite amount of time. Inspired by these practical backgrounds, this article is mainly contributed to fixed/preassigned-time (FIX/PT) output synchronization of homogeneous complex networks with output coupling. Firstly, a kind of observable output coupling is introduced into the modeling of CNs based on a type of output coefficient matrices having a general form. Additionally, some distributed dynamic event-triggered control (EVTC) schemes are developed based on the local output information to reduce communication frequency and resource waste. By using FIX stability theory and the method of reduction to absurdity, some concise criteria are derived to reach FIX/PT synchronization and the Zeno behavior is excluded. Note that the effect of the output matrix on network synchronization is revealed and the analysis difficulty caused by the unknown or non-differentiable synchronous state is effectively solved. At last, two numerical experiments and numerical comparisons are presented.
引用
收藏
页数:24
相关论文
共 47 条
[1]   Power grid vulnerability: A complex network approach [J].
Arianos, S. ;
Bompard, E. ;
Carbone, A. ;
Xue, F. .
CHAOS, 2009, 19 (01)
[2]   Adaptive Leader-Follower Synchronization Over Heterogeneous and Uncertain Networks of Linear Systems Without Distributed Observer [J].
Azzollini, Ilario Antonio ;
Yu, Wenwu ;
Yuan, Shuai ;
Baldi, Simone .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2021, 66 (04) :1925-1931
[3]   Leaderless Synchronization of Heterogeneous Oscillators by Adaptively Learning the Group Model [J].
Baldi, Simone ;
Frasca, Paolo .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2020, 65 (01) :412-418
[4]   Adaptive Synchronization of Fractional-Order Output-Coupling Neural Networks via Quantized Output Control [J].
Bao, Haibo ;
Park, Ju H. ;
Cao, Jinde .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (07) :3230-3239
[5]   Fixed-time dual-channel event-triggered secure quasi-synchronization of coupled memristive neural networks [J].
Bao, Yuangui ;
Zhang, Yijun .
JOURNAL OF THE FRANKLIN INSTITUTE, 2021, 358 (18) :10052-10078
[6]   Adaptive fixed-time output synchronization for complex dynamical networks with multi-weights [J].
Cao, Yuting ;
Zhao, Linhao ;
Zhong, Qishui ;
Wen, Shiping ;
Shi, Kaibo ;
Xiao, Jianying ;
Huang, Tingwen .
NEURAL NETWORKS, 2023, 163 :28-39
[7]   Joint Channel and Link Selection in Formation-Keeping UAV Networks: A Two-Way Consensus Game [J].
Chen, Jiaxin ;
Chen, Ping ;
Xu, Yuhua ;
Qi, Nan ;
Fang, Tao ;
Dong, Chao ;
Wu, Qihui .
IEEE TRANSACTIONS ON MOBILE COMPUTING, 2022, 21 (08) :2861-2875
[8]   Output Synchronization on Strongly Connected Graphs [J].
Chopra, Nikhil .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2012, 57 (11) :2896-2901
[9]   Distributed Event-Triggered Control for Multi-Agent Systems [J].
Dimarogonas, Dimos V. ;
Frazzoli, Emilio ;
Johansson, Karl H. .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2012, 57 (05) :1291-1297
[10]   Prefixed-Time Local Intermittent Sampling Synchronization of Stochastic Multicoupling Delay Reaction-Diffusion Dynamic Networks [J].
Ding, Kui ;
Zhu, Quanxin ;
Huang, Tingwen .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 35 (01) :718-732