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基于全过程隐私保护的多智能体系统平均一致性

纪良浩 唐少洪 郭兴 解燕

纪良浩, 唐少洪, 郭兴, 解燕. 基于全过程隐私保护的多智能体系统平均一致性. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240471
引用本文: 纪良浩, 唐少洪, 郭兴, 解燕. 基于全过程隐私保护的多智能体系统平均一致性. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240471
Ji Liang-Hao, Tang Shao-Hong, Guo Xing, Xie Yan. Average consensus in multi-agent systems based on whole-process privacy protection. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240471
Citation: Ji Liang-Hao, Tang Shao-Hong, Guo Xing, Xie Yan. Average consensus in multi-agent systems based on whole-process privacy protection. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240471

基于全过程隐私保护的多智能体系统平均一致性

doi: 10.16383/j.aas.c240471 cstr: 32138.14.j.aas.c240471
基金项目: 国家自然科学基金(62276036),重庆市教委科学技术研究项目(KJQN202400627),重庆市自然科学基金创新发展联合基金重点项目(CSTB2024NSCQ-LZX0118),重庆市教委科技重大项目(M202100602), 重庆市教委科学技术研究项目(KJQN202400627)
详细信息
    作者简介:

    纪良浩:重庆邮电大学教授. 2014年获得重庆大学博士学位. 主要研究方向为多智能体系统和智能信息处理. 本文通信作者. E-mail: Jilh@cqupt.edu.cn

    唐少洪:重庆邮电大学硕士研究生. 2022年获得沈阳师范大学软件学院学士学位. 主要研究方向为多智能体系统的一致性控制和隐私保护. E-mail: s220201088@stu.cqupt.edu.cn

    郭兴:重庆邮电大学讲师. 2020年获得东南大学博士学位. 主要研究方向为多智能体系统的分布式控制和网络安全控制. E-mail: Guoxing@cqupt.edu.cn

    解燕:重庆邮电大学博士研究生. 2022年获得重庆科技大学硕士学位. 主要研究方向为多智能体系统弹性控制. E-mail: d220201014@stu.cqupt.edu.cn

Average Consensus in Multi-agent Systems Based on Whole-process Privacy Protection

Funds: Supported by the National Natural Science Foundation of China under Grant No. 62276036, in part by the Innovation and Development Joint Fund Project of Chongqing Natural Science Foundation under Grant No. CSTB2024NSCQ-LZX0118, in part by the Major Project of Scientific and Technological Research Program of Chongqing Municipal Education Commission under Grant No. KJZD-M202100602, and in part by the Science and Technology Research Project of the Chongqing Education Commission under Grant No. KJQN202400627
More Information
    Author Bio:

    JI Liang-Hao Professor at Chongqing University of Posts and Telecommunications. He obtained the doctoral degree from Chongqing University in 2014. The main research focuses on multi-agent systems and intelligent information processing. The corresponding author of this article. Corresponding author of this paper

    TANG Shao-Hong Master student at Chongqing University of Posts and Telecommunications. He obtained the bachelor's degree from the School of Software, Shenyang Normal University in 2022. His main research interests lie in the consensus control of multi-agent systems and privacy protection

    TANG Shao-Hong Master student at Chongqing University of Posts and Telecommunications. He obtained the bachelor's degree from the School of Software, Shenyang Normal University in 2022. His main research interests lie in the consensus control of multi-agent systems and privacy protection

    XIE Yan Ph.D. candidate at Chongqing University of Posts and Telecommunications. She received her master degree from Chongqing University of Science and Technology in 2022. Her research interest covers the resilient control of multi-agent systems

  • 摘要: 针对通信网络可能遭受多邻居联合窃听的多智能体系统, 研究其基于全过程隐私保护的平均一致性问题, 具体包括保护智能体的初始状态以及智能体在实现平均一致性整个过程中的实时状态.不同于现有的隐私保护平均一致性算法仅能保护智能体的初始状态且无法抵御联合窃听, 提出了基于虚拟子网和非消失扰动的全过程隐私保护平均一致性算法. 在所提算法下, 即使智能体的所有信道都被窃听, 多智能体系统的平均一致性仍然可以被实现且智能体的状态可以得到全过程保护. 最后, 通过几个数值仿真实验验证了算法的有效性.
  • 图  1  非联合窃听下的网络示意图

    Fig.  1  Schematic of the network under non-collaborative eavesdropping

    图  4  完全联合窃听下的网络示意图

    Fig.  4  The diagram of the network under full collaborative eavesdropping

    图  2  弱联合窃听下的网络示意图

    Fig.  2  Schematic of the network under weak collaborative eavesdropping

    图  3  强联合窃听下的网络示意图

    Fig.  3  The diagram of the network under strong collaborative eavesdropping

    图  5  算法示意图

    Fig.  5  Schematic diagram of the algorithm

    图  6  强联合窃听下的简化示意图

    Fig.  6  Simplified schematic under strong collaborative eavesdropping

    图  7  5个节点组成的多智能体系统通信拓扑

    Fig.  7  Network topology of multi-agent system with 5 nodes

    图  8  在(20)下系统各节点输出值的变化轨迹

    Fig.  8  Trajectories of the output values of each node of the system under (20)

    图  9  在实现方式(20)和(21)下:智能体的输出轨迹$y_i^{\alpha}(t)$和$\bar{y}_i^{\alpha}(t)$

    Fig.  9  The information under realizations (20) and (21): $y_i^{\alpha}(t)$ and $\bar{y}_i^{\alpha}(t)$

    图  10  在实现方式(20)和(21)下:虚拟节点的输出轨迹$y_i^{\beta}(t)$和$\bar{y}_i^{\beta}(t)$

    Fig.  10  The information under realizations (20) and (21): $y_i^{\beta}(t)$ and $\bar{y}_i^{\beta}(t)$

    图  11  在实现方式(20)和(21)下:智能体的实时状态轨迹$x_i^{\alpha}(t)$和$\bar{x}_i^{\alpha}(t)$

    Fig.  11  The information under realizations (20) and (21): $x_i^{\alpha}(t)$ and $\bar{x}_i^{\alpha}(t)$

    图  12  在实现方式(20)和(21)下:虚拟节点的状态轨迹$x_i^{\beta}(t)$和$\bar{x}_i^{\beta}(t)$

    Fig.  12  The information under realizations (20) and (21): $x_i^{\beta}(t)$ and $\bar{x}_i^{\beta}(t)$

    图  13  在实现方式(20)和(22)下:节点1的实时状态轨迹$x_i^{\alpha}(t)$和$\bar{\bar{x}}_i^{\alpha}(t)$

    Fig.  13  The information under realizations (20) and (22): $x_i^{\alpha}(t)$ and $\bar{\bar{x}}_i^{\alpha}(t)$

    图  14  在[31]的方法下, 每个节点的输出轨迹$y_i(t)$

    Fig.  14  The output trajectory of each node under the method of [31]

    图  15  $Z_2(t)$在本文和[31]下的轨迹

    Fig.  15  $Z_2(t)$ trajectories under the method of this paper and [31]

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  • 收稿日期:  2024-07-03
  • 录用日期:  2025-01-17
  • 网络出版日期:  2025-05-16

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