基于事件触发的多智能体系统差分隐私保护跟踪控制

谢光强; 胡明昱; 李杨

doi:10.12052/gdutxb.240022

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基于事件触发的多智能体系统差分隐私保护跟踪控制

控制科学与工程 | 更新时间：2026-05-27

- 基于事件触发的多智能体系统差分隐私保护跟踪控制
- Event-triggered Differential Privacy Protection Tracking Control
- 广东工业大学学报 2025年42卷第2期页码：70-80
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  谢光强(1979–)，男，教授，博士，主要研究方向为多智能体、智能体控制，E-mail：xiegq@gdut.edu.cn
  李杨(1980–)，女，教授，博士，主要研究方向为多智能体、差分隐私保护，E-mail：liyang@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62006047，618760439);广东省重点研发项目(2021B0101220004)
- DOI：10.12052/gdutxb.240022
  中图分类号： TP391
- 纸质出版：2025
- 稿件说明：
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谢光强, 胡明昱, 李杨. 基于事件触发的多智能体系统差分隐私保护跟踪控制[J]. 广东工业大学学报, 2025, 42(2): 70–80.

Xie Guangqiang, Hu Mingyu, Li Yang. Event-triggered Differential Privacy Protection Tracking Control[J]. 2025, 42(2): 70-80.
谢光强, 胡明昱, 李杨. 基于事件触发的多智能体系统差分隐私保护跟踪控制[J]. 广东工业大学学报, 2025, 42(2): 70–80. DOI： 10.12052/gdutxb.240022

Xie Guangqiang, Hu Mingyu, Li Yang. Event-triggered Differential Privacy Protection Tracking Control[J]. 2025, 42(2): 70-80. DOI： 10.12052/gdutxb.240022.

摘要

为了应对传统跟踪控制方法可能引起的窃听者攻击风险，多智能体系统跟踪控制研究需要考虑隐私保护问题。本文在同时存在领导者与跟随者的多智能体系统中，针对跟踪控制隐私保护问题，提出了基于事件触发的分布式差分隐私保护跟踪控制方法(Event-triggered Distributed Differential Privacy Protection Tracking Control

EDPTC)。该方法旨在时刻保障领导者与跟随者的状态隐私，同时实现均方跟踪。考虑到领导者与跟随者状态更新的差异，基于敏感度上界定理，分别设计了适应各自特点的隐私保护机制：跟随者的递减噪声机制(The Decreasing Noise Mechanism of Followers

DNMF) 和领导者的随机噪声机制(The Random Noise Mechanism of Leaders

RNML)。此外，为减少随机噪声对控制效果的影响，引入领导者状态估计算法，并设计分布式事件触发器以降低通信频率。通过矩阵分析和概率论，证明了EDPTC的隐私性并推导出了其实现均方跟踪的充分条件。最后，通过一系列数值仿真验证了方法的有效性。

Abstract

To counter the risk of eavesdropper attacks inherent in traditional tracking control methods

research on tracking control in multi-agent systems must incorporate privacy protection considerations. This study introduces an event-triggered distributed differential privacy protection tracking control (EDPTC) method in multi-agent systems featuring both leaders and followers

aimed at addressing privacy protection issues in tracking control. The method is designed to ensure the privacy of the states of leaders and followers at all times while achieving mean square tracking. Given the differences in state updates between leaders and followers

privacy protection mechanisms tailored to each party are developed based on the sensitivity upper bound theorem: the decreasing noise mechanism for followers (DNMF) and the random noise mechanism for leaders (RNML) . Moreover

to minimize the impact of random noise on control performance

a leader state estimation algorithm is introduced

and a distributed event trigger is designed to reduce the communication frequency. Additionally

through matrix analysis and probability theory

the privacy of the EDPTC is proven

and sufficient conditions for achieving mean square tracking are derived. Finally

a series of numerical simulations validate the effectiveness of the proposed method.

关键词

Keywords

references

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