Distributed Extended State Estimation for Complex Networks With Nonlinear Uncertainty

被引：18

作者：

Peng, Hui ^{[1
]}

Zeng, Boru ^{[1
]}

Yang, Lixin ^{[1
]}

Xu, Yong ^{[1
]}

Lu, Renquan ^{[1
]}

机构：

[1] Guangdong Univ Technol, Sch Automat, Guangdong Prov Key Lab Intelligent Decis & Coopera, Guangzhou 510006, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2023年 / 34卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Complex networks (CNs); distributed extended state estimator; nonlinear uncertainty; SYNCHRONIZATION; SYSTEMS; OSCILLATORS; ROBUSTNESS;

D O I：

10.1109/TNNLS.2021.3131661

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article studies the distributed state estimation issue for complex networks with nonlinear uncertainty. The extended state approach is used to deal with the nonlinear uncertainty. The distributed state predictor is designed based on the extended state system model, and the distributed state estimator is designed by using the measurement of the corresponding node. The prediction error and the estimation error are derived. The prediction error covariance (PEC) is obtained in terms of the recursive Riccati equation, and the upper bound of the PEC is minimized by designing an optimal estimator gain. With the vectorization approach, a sufficient condition concerning stability of the upper bound is developed. Finally, a numerical example is presented to illustrate the effectiveness of the designed extended state estimator.

引用

页码：5952 / 5960

页数：9

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