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2021影响因子

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## 留言板

 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
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

## 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)
###### 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

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##### 出版历程
• 收稿日期:  2021-09-25
• 录用日期:  2022-10-29
• 网络出版日期:  2022-12-15

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