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              基于時滯測量的復雜網絡分布式狀態估計研究

              滕達 徐雍 鮑鴻 王卓 魯仁全

              滕達, 徐雍, 鮑鴻, 王卓, 魯仁全. 基于時滯測量的復雜網絡分布式狀態估計研究. 自動化學報, 2024, 50(4): 841?850 doi: 10.16383/j.aas.c210921
              引用本文: 滕達, 徐雍, 鮑鴻, 王卓, 魯仁全. 基于時滯測量的復雜網絡分布式狀態估計研究. 自動化學報, 2024, 50(4): 841?850 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, 2024, 50(4): 841?850 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, 2024, 50(4): 841?850 doi: 10.16383/j.aas.c210921

              基于時滯測量的復雜網絡分布式狀態估計研究

              doi: 10.16383/j.aas.c210921
              基金項目: 廣東省重點領域研發計劃(2021B0101410005), 國家自然科學基金 (62121004, 62006043, U22A2044, 61673041), 廣東省特支計劃本土創新創業團隊(2019BT02X353), 廣東省基礎與應用基礎研究基金項目(2021B1515420008)資助
              詳細信息
                作者簡介:

                滕達:廣東工業大學碩士研究生. 2015年獲得中國礦業大學徐海學院學士學位. 主要研究方向為具有測量受限的復雜網絡狀態估計. E-mail: 18852141796@163.com

                徐雍:廣東工業大學自動化學院教授. 2007年獲得南昌航空大學信息工程學士學位, 2010年獲得杭州電子科技大學控制科學與工程碩士學位, 2014年獲得浙江大學控制科學和工程博士學位. 主要研究方向為網絡化控制系統, 狀態估計與濾波, 水空兩棲無人機和智能無人艇. 本文通信作者. E-mail: xuyong809@163.com

                鮑鴻:廣東工業大學自動化學院教授. 1999年獲得華中科技大學控制科學與工程博士學位. 主要研究方向為復雜系統控制理論研究. E-mail: bhong@gdut.edu.cnk

                王卓:北京航空航天大學儀器科學與光電工程學院教授. 2013年獲得美國伊利諾伊大學芝加哥分校電子與計算機工程系博士學位. 主要研究方向為基于數據的系統辨識、建模、分析、優化與控制, 自適應動態規劃方法, 非線性自適應控制, 基于原子自旋效應的慣性/磁場測量技術, 自旋原子系綜控制(操控)方法. E-mail: zhuowang@buaa.edu.cn

                魯仁全:廣東工業大學自動化學院教授. 2004年獲得浙江大學控制科學與工程專業博士學位. 主要研究方向為復雜系統, 網絡控制系統, 非線性系統, 變結構無人機, 智能無人車, 多旋翼大型無人機, 無人自主系統的編隊與協同控制. 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:

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

                XU Yong Professor at the School of Automation, Guangdong University of Technology. He received his bachelor degree in information engineering from Nanchang Hangkong University in 2007, his master degree in control science and engineering from Hangzhou Dianzi University in 2010, and his Ph.D. degree in control science and engineering from Zhejiang University in 2014. His research interest covers networked control systems, state estimation and filtering, water and air amphibious unmanned aerial vehicle, and intelligent unmanned boat. Corresponding author of this paper

                BAO Hong Professor at the School of Automation, 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 from the Electrical and Computer Engineering Department, University of Illinois at Chicago, USA, in 2013. His research interest covers data-based system identification, modeling, analysis, optimization and control, adaptive dynamic programming methods, nonlinear adaptive control, atomic-spin-effect-based inertial/magnetic field measurement technology, and atomic ensemble control (manipulation) methods

                LU Ren-Quan Professor at the School of Automation, Guangdong University of Technology. He received his Ph.D. degree in control science and engineering from Zhejiang University in 2004. His research interest covers complex systems, networked control systems, nonlinear systems, variable structure unmanned aerial vehicles (UAVs), intelligent unmanned vehicles, large multi-rotor UAVS, and formation and cooperative control of unmanned autonomous systems

              • 摘要: 研究一類存在一步隨機時滯的復雜網絡分布式狀態估計問題, 采用伯努利隨機變量刻畫測量值的隨機時滯情況. 基于復雜網絡模型和不可靠測量值, 分別設計復雜網絡的狀態預測器和分布式狀態估計器, 基于楊氏不等式消除節點之間的耦合項, 通過優化楊氏不等式引進的參數, 優化狀態預測協方差. 通過設計估計器增益, 獲得狀態估計誤差協方差, 同時結合預測誤差協方差, 獲得狀態估計誤差協方差的迭代公式, 并給出估計誤差協方差穩定的充分條件. 最后, 對由小車組成的耦合系統進行數值仿真, 驗證所設計估計器的有效性.
              • 圖  1  小車耦合系統的拓撲

                Fig.  1  The topology of coupled systems consisted of vehicles

                圖  2  小車的實際運動軌跡

                Fig.  2  The actual motion trajectories of vehicles

                圖  3  優化和未優化的$ \gamma_{1,i,k} $

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

                圖  4  基于優化和未優化$ \gamma_{1,1,k} $的第1個節點的估計誤差協方差上界的跡和MSE

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

                圖  5  基于優化和未優化$ \gamma_{1,2,k} $的第2個節點的估計誤差協方差上界的跡和MSE

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

                圖  6  基于優化和未優化$ \gamma_{1,3,k} $的第3個節點的估計誤差協方差上界的跡和MSE

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

                圖  7  基于優化和未優化$ \gamma_{1,4,k} $的第4個節點的估計誤差協方差上界的跡和MSE

                Fig.  7  The trace of 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  基于優化和未優化的$\gamma_{1,i,k}$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

                節點$i$未優化$\rm{tr}(P_{i,k|k})$上界優化后$\rm{tr}(P_{i,k|k})$上界優化幅度(%)
                10.06790.06228.50
                20.06860.06308.23
                30.08060.07339.04
                40.07680.07176.60
                下載: 導出CSV

                表  2  基于優化和未優化的$\gamma_{1,i,k}$的MSE$_{i,k|k}$

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

                節點$i$未優化MSE$_{i,k|k}$均值優化后MSE$_{i,k|k}$均值優化幅度(%)
                10.03570.03385.23
                20.03640.03474.82
                30.04240.04005.73
                40.04560.04383.83
                下載: 導出CSV
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                        • 收稿日期:  2021-09-25
                        • 錄用日期:  2022-10-29
                        • 網絡出版日期:  2022-12-15
                        • 刊出日期:  2024-04-26

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