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基于时滞测量的复杂网络分布式状态估计研究

滕达 徐雍 鲍鸿 王卓 鲁仁全

滕达, 徐雍, 鲍鸿, 王卓, 鲁仁全. 基于时滞测量的复杂网络分布式状态估计研究. 自动化学报, 2022, 48(x): 1−10 doi: 10.16383/j.aas.c210921
引用本文: 滕达, 徐雍, 鲍鸿, 王卓, 鲁仁全. 基于时滞测量的复杂网络分布式状态估计研究. 自动化学报, 2022, 48(x): 1−10 doi: 10.16383/j.aas.c210921
Teng Da, Xu Yong, Bao Hong, Wang Zhuo, Lu Ren-Quan. Distributed state estimation for complex networks with delayed measurements. Acta Automatica Sinica, 2022, 48(x): 1−10 doi: 10.16383/j.aas.c210921
Citation: Teng Da, Xu Yong, Bao Hong, Wang Zhuo, Lu Ren-Quan. Distributed state estimation for complex networks with delayed measurements. Acta Automatica Sinica, 2022, 48(x): 1−10 doi: 10.16383/j.aas.c210921

基于时滞测量的复杂网络分布式状态估计研究

doi: 10.16383/j.aas.c210921
基金项目: 广东省重点领域研发计划(2021B0101410005), 国家自然科学基金 (62121004, 62006043, U22A2044, 61673041), 广东省特支计划本土创新创业团队(2019BT02X353), 广东省基础与应用基础研究基金项目(2021B1515420008)资助
详细信息
    作者简介:

    滕达:广东工业大学硕士研究生. 2015年获得中国矿业大学徐海学院学士学位. 主要研究方向为具有测量受限的复杂网络状态估计. E-mail: 18852141796@163.com

    徐雍:1983年出生于中国浙江省. 广东工业大学自动化学院教授. 2007年获得中国南昌航空大学信息工程学士学位, 2010年获得中国杭州电子大学控制科学与工程硕士学位, 2014年获得中国浙江大学控制科学和工程博士学位. 主要研究方向为网络化控制系统、状态估计与滤波、水空两栖无人机和智能无人艇. 本文通信作者. E-mail: xuyong809@163.com

    鲍鸿:广东工业大学自动化学院教授. 1999年获得华中科技大学控制科学与工程博士. 主要研究方向为复杂系统控制理论研究. E-mail: bhong@gdut.edu.cnk

    王卓:北京航空航天大学仪器科学与光电工程学院教授. 2013年获得美国伊利诺伊大学芝加哥分校电子与计算机工程系博士学位. 主要研究方向为基于数据的系统辨识、建模、分析、优化与控制, 自适应动态规划方法, 非线性自适应控制, 基于原子自旋效应的惯性/磁场测量技术, 自旋原子系综控制(操控)方法. E-mail: zhuowang@buaa.edu.cn

    鲁仁全:广东工业大学自动化学院教授. 2004年获得中国浙江大学控制科学与工程专业博士学位. 主要研究方向为复杂系统, 网络控制系统, 非线性系统, 变结构无人机, 智能无人车, 多旋翼大型无人机, 无人自主系统的编队与协同控制. E-mail: rqlu@gdut.edu.cn

Distributed State Estimation for Complex Networks With Delayed Measurements

Funds: Supported by Key Area Research and Development Program of Guangdong Province (2021B0101410005), National Natural Science Foundation of China (62121004, 62006043, U22A2044, 61673041), the Local Innovative and Research Teams Project of Guangdong Special Support Program (2019BT02X353), and GuangDong Basic and Applied Basic Research Foundation (2021B1515420008)
More Information
    Author Bio:

    TEND Da Master student of Guangdong University of Technology. He received his bachelor degree from Xuhai College of China University of Mining and Technology in 2015. His main research interest is complex network state estimation with measurement constraints

    XU Yong born in Zhejiang Province, China, in 1983. Professor of Automation College of Guangdong University of Technology. He received the B.S. degree in information engineering from Nanchang Hangkong University, Nanchang, China, in 2007, the M.S. degree in control science and engineering from Hangzhou Dianzi University, Hangzhou, China, in 2010, and the Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2014. His research interests include networked control systems, state estimation and filtering, amphibious unmanned aerial vehicle, and intelligent unmanned boat. Corresponding author of this paper

    BAO Hong Professor of Automation College of Guangdong University of Technology. She received her Ph.D. degree in Control Science and Engineering from Huazhong University of Science and Technology in 1999. Her main research interest is complex system control theory

    WANG Zhuo Professor at the School of Instrumentation and Optoelectronic Engineering, Beihang University. He received his Ph.D. degree in Electrical and Computer Engineering Department from University of Illinois at Chicago, in 2013. His research interest covers data-based system identiflcation, modeling, analysis, optimization and control, adaptive dynamic programming methods, nonlinear adaptive control, atomic-spin-efiect-based inertial/magnetic field measurement technology, and atomic ensemble control (manipulation) methods

    LU Ren-Quan Professor of Automation College of Guangdong University of Technology. He received his Ph.D. degree in control science and engineering from Zhejiang University, China, in 2004. His research interests include complex systems, networked control systems, nonlinear systems, variable structure UAVs, intelligent unmanned vehicles, large multi-rotor UAVS, and formation and cooperative control of unmanned autonomous systems

  • 摘要: 研究了一类存在一步随机时滞的复杂网络分布式状态估计问题, 采用伯努利随机变量刻画测量值的随机时滞情况. 基于复杂网络模型和不可靠测量值, 分别设计了复杂网络的状态预测器和分布式状态估计器, 基于杨氏不等式消除了节点之间的耦合项, 通过优化杨氏不等式引进的参数, 优化了状态预测协方差. 通过设计估计器增益, 获得了状态估计误差协方差, 同时结合预测误差协方差, 获得了状态估计误差协方差的迭代公式, 并给出了估计误差协方差稳定的充分条件. 最后, 对由小车组成的耦合系统进行数值仿真, 验证了所设计估计器的有效性.
  • 图  1  小车耦合系统的拓扑

    Fig.  1  The topology of coupled systems consisted of vehicles

    图  2  小车的实际运动轨迹

    Fig.  2  The trajectories of vehicles

    图  3  优化和未优化的$ \gamma_{1,i,k} $

    Fig.  3  $ \gamma_{1,i,k} $ with and without optimization

    图  4  基于优化和未优化$ \gamma_{1,1,k} $的第1个节点的估计误差协方差上界和MSE

    Fig.  4  The upper bound of the estimation error covariance and the MSE of the node 1 based on $ \gamma_{1,1,k} $ with and without optimization

    图  5  基于优化和未优化$ \gamma_{1,2,k} $的第2个节点的估计误差协方差上界和MSE

    Fig.  5  The upper bound of the estimation error covariance and the MSE of the node 2 based on $ \gamma_{1,2,k} $ with and without optimization

    图  6  基于优化和未优化$ \gamma_{1,3,k} $的第3个节点的估计误差协方差上界和MSE

    Fig.  6  The upper bound of the estimation error covariance and the MSE of the node 3 based on $ \gamma_{1,3,k} $ with and without optimization

    图  7  基于优化和未优化$ \gamma_{1,4,k} $的第4个节点的估计误差协方差上界和MSE

    Fig.  7  The upper bound of the estimation error covariance and the MSE of the node 4 based on $ \gamma_{1,4,k} $ with and without optimization

    表  1  基于优化和未优化的$\gamma_{1,i,k}$的上界$\rm{tr}(P_{i,k|k})$

    Table  1  The upper bound $\rm{tr}(P_{i,k|k})$ based on $\gamma_{1,i,k}$ with and without optimization

    节点$i$未优化$\rm{tr}(P_{i,k|k})$上界优化后$\rm{tr}(P_{i,k|k})$上界优化幅度
    10.06790.06228.50%
    20.06860.06308.23%
    30.08060.07339.04%
    40.07680.07176.60%
    下载: 导出CSV

    表  2  基于优化和未优化的$\gamma_{1,i,k}$的MSE$_{i,k|k}$

    Table  2  The MSE$_{i,k|k}$ based on $\gamma_{1,i,k}$ with and without optimization

    节点$i$未优化MSE$_{i,k|k}$均值优化后MSE$_{i,k|k}$均值优化幅度
    10.03570.03385.23%
    20.03640.03474.82%
    30.04240.04005.73%
    40.04560.04383.83%
    下载: 导出CSV
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  • 收稿日期:  2021-09-25
  • 录用日期:  2022-10-29
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