异构多智能体网络拓扑可辨识性

王立夫; 高聪; 郭戈; 孔芝

doi:10.16383/j.aas.c240416

异构多智能体网络拓扑可辨识性

doi: 10.16383/j.aas.c240416 cstr: 32138.14.j.aas.c240416

王立夫^1,,
高聪^1,,
郭戈^{1, 2,},
孔芝^1,

1.
东北大学秦皇岛分校控制工程学院河北秦皇岛 066004
2.
东北大学流程工业综合自动化国家重点实验室辽宁沈阳 110004

基金项目: 国家自然科学基金(61573077, U1808205), 国家留学基金(202308130119), 河北省自然科学基金(F2022501005)资助

详细信息

作者简介:
王立夫：东北大学秦皇岛分校副教授. 主要研究方向为复杂网络, 同步控制, 能控性, 交通网络. E-mail: wlfkz@neuq.edu.cn

高聪：东北大学秦皇岛分校硕士研究生. 主要研究方向为复杂网络可辨识性. E-mail: 2372269@stu.neu.edu.cn

郭戈：东北大学教授. 主要研究方向为智能交通系统, 交通大数据分析, 人工智能应用, 信息物理系统. 本文通信作者. E-mail: geguo@yeah.net

孔芝：东北大学秦皇岛分校副教授. 主要研究方向为知识发现, 决策分析, 智能优化算法, 复杂网络. E-mail: kongz@neuq.edu.cn

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- 文章访问数: 21
- HTML全文浏览量: 16
- 被引次数: 0
出版历程
- 收稿日期: 2024-06-29
- 录用日期: 2024-11-21
- 网络出版日期: 2024-12-19

Discernibility of Heterogeneous Multi-agent Networks Topology

WANG Li-Fu^1
,,
GAO Cong^1
,,
GUO Ge^{1, 2
,},
KONG Zhi^1
,

1.
School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004
2.
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004

Funds: Supported by National Natural Science Foundation of China (61573077, U1808205), China Scholarship Council (202308130119), and Natural Science Foundation of Hebei Province (F2022501005)

More Information

Author Bio:
WANG Li-Fu　Associate professor of Northeastern University at Qinhuangdao. His research interests include complex networks, synchron-ous control, controllability, and traffic networks

GAO Cong　Master student of Northeastern University at Qinhuangdao. Her research interest is discernibility of complex networks

GUO Ge　Professor of Northeastern University. His research interests include intelligent transportation systems, traffic big data analysis, artificial intelligence applications, and information physical systems. Corresponding author of this paper

KONG Zhi　Associate professor of Northeastern University at Qinhuang-dao. Her research interests include knowledge discovery, decision analy-sis, intelligent optimization algorithms, and complex networks

摘要

摘要: 研究了高维线性时不变动力学系统构成的具有加权有向的多智能体网络拓扑变化可辨识性. 这些网络智能体动力学和内耦合矩阵均具有异构性. 分析了异构内耦合矩阵对网络拓扑可辨识性的影响, 并发现网络拓扑结构的可辨识性与智能体之间的内耦合矩阵相关. 当内耦合矩阵由同构变为异构时, 网络拓扑的可辨识性可能发生变化, 既可能由可辨识变为不可辨识, 也可能由不可辨识变为可辨识. 针对一般网络结构, 提出了一些充分和必要的条件以验证拓扑变化的可辨识性. 此外, 针对有向链状网络、有向星型网络以及有向环状网络等几种典型网络结构, 分别给出了相应的可辨识性条件. 通过实际案例验证了所提条件的合理性和有效性.
- 可辨识性 /
- 异构网络 /
- 内耦合矩阵 /
- 拓扑变化
Abstract: This paper investigates the discernibility of topology variations in weighted directed agent networks composed of high-dimensional linear time invariant dynamical systems. The dynamics and inner-coupling matrix of these agent networks are both heterogeneous. We analyze the influence of heterogeneous inner-coupling matrix on the discernibility of network topology and find that the discernibility of network topology structure is related to the inner-coupling matrix between agents. When the inner-coupling matrix changes from homogeneous to heterogeneous, the discernibility of network topology may also change, from discernible to undiscernible or from undiscernible to discernible. For general network structures, this paper proposes some sufficient and necessary conditions to verify the discernibility of topology variations. Corresponding discernibility conditions are provided for several typical network structures, such as directed chain networks, directed star networks, and directed ring networks. The rationality and effectiveness of the conditions proposed in this paper are verified through practical cases.
- Discernibility /
- heterogeneous networks /
- inner-coupling matrix /
- topological variations

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图 1 增加了一条连边的有向网络

Fig. 1 A directed network with an edge added

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图 2 删除了一条连边的有向网络

Fig. 2 A directed network with an edge deleted

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图 3 一个有向链状网络

Fig. 3 A directed chain network

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图 4 一个有向星形网络

Fig. 4 A directed star network

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图 5 一个有向环状网络

Fig. 5 A directed circle network

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图 6 一个具有两个智能体组成的有向环状网络系统

Fig. 6 A directed circular network system consisting of two agents

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图 7 无人驾驶车辆系统

Fig. 7 Autonomous vehicle system

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图 8 编队避障无人机系统

Fig. 8 Formation obstacle avoidance UAV system

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