Data-Driven Strategies for Modal Consensus and Output Synchronization

被引:0
|
作者
Monti, Andrea [1 ]
Galeani, Sergio [2 ]
Possieri, Corrado [2 ,3 ]
Sassano, Mario [2 ]
机构
[1] German Aerosp Ctr DLR, Inst Software Technol, D-38108 Braunschweig, Germany
[2] Univ Roma Tor Vegata, Dipartimento Ingn Civile & Ingn Informat DICII, I-00133 Rome, Italy
[3] Ist Anal Sistemi Informat A Ruberti Consiglio Nazl, I-00185 Rome, Italy
来源
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS | 2024年 / 11卷 / 03期
关键词
Synchronization; Multi-agent systems; Steady-state; Network systems; Trajectory; Task analysis; Symbols; Consensus; data-driven control; linear systems; multiagent systems;
D O I
10.1109/TCNS.2023.3338255
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this article, a control design strategy to achieve modal consensus and synchronization in a multiagent system is envisioned. The proposed solution relies only on input/output data locally collected by each agent and does not require any a priori knowledge of the underlying dynamics. Furthermore, while agreement on a prescribed modal content can be achieved for any generic initial conditions of the agents, it is shown that the synchronization task requires instead to address the problem of transferring, in finite time, the state of a system from an initial state to a desired terminal state in a completely data-driven framework. This control problem, interesting per se, is therefore tackled and solved here. The above concepts are then illustrated by means of numerical case studies.
引用
收藏
页码:1527 / 1536
页数:10
相关论文
共 50 条
  • [21] Data-Driven H∞ Output Consensus for Heterogeneous Multiagent Systems Under Switching Topology via Reinforcement Learning
    Liu, Qiwei
    Yan, Huaicheng
    Zhang, Hao
    Wang, Meng
    Tian, Yongxiao
    IEEE TRANSACTIONS ON CYBERNETICS, 2024, : 7865 - 7876
  • [22] Data-driven experimental modal analysis by Dynamic Mode Decomposition
    Saito, Akira
    Kuno, Tomohiro
    JOURNAL OF SOUND AND VIBRATION, 2020, 481
  • [23] Characteristic value decomposition: A unifying paradigm for data-driven modal
    Li, He-Wen-Xuan
    Stein, Dalton L.
    Chelidze, David
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2025, 222
  • [24] Data-driven spatiotemporal modal decomposition for time frequency analysis
    Hirsh, Seth M.
    Brunton, Bingni W.
    Kutz, J. Nathan
    APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2020, 49 (03) : 771 - 790
  • [25] On the intrinsic time scales involved in synchronization: A data-driven approach
    Chavez, M
    Adam, C
    Navarro, V
    Boccaletti, S
    Martinerie, J
    CHAOS, 2005, 15 (02)
  • [26] A Data-Driven Approach for Grid Synchronization Based on Deep Learning
    Miranbeigi, Mohammadreza
    Kandula, Prasad
    Divan, Deepak
    2021 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE), 2021, : 2985 - 2991
  • [27] Data-Driven Optimal Synchronization for Complex Networks With Unknown Dynamics
    Hu, Wenjie
    Gao, Luli
    Dong, Tao
    IEEE ACCESS, 2020, 8 : 224083 - 224091
  • [28] Data-Driven Nonlinear Modal Analysis: A Deep Learning Approach
    Li, Shanwu
    Yang, Yongchao
    NONLINEAR STRUCTURES & SYSTEMS, VOL 1, 2023, : 229 - 231
  • [29] Data-Driven Strategies for selective data transmission in sensor networks
    Battistelli, Giorgio
    Benavoli, Alessio
    Chisci, Luigi
    2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2012, : 800 - 805
  • [30] Future government data strategies: data-driven enterprise or data steward?
    van Donge, W.
    Bharosa, N.
    Janssen, M. F. W. H. A.
    PROCEEDINGS OF THE 21ST ANNUAL INTERNATIONAL CONFERENCE ON DIGITAL GOVERNMENT RESEARCH, DGO 2020, 2020, : 196 - 204