Dynamic event-triggered state estimation for time-delayed spatial-temporal networks under encoding-decoding scheme

被引:5
|
作者
Sun, Jie [1 ,2 ]
Shen, Bo [1 ,2 ]
Liu, Yurong [3 ]
Alsaadi, Fuad E. [4 ]
机构
[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China
[2] Minist Educ, Engn Res Ctr Digitalized Text & Fash Technol, Shanghai 201620, Peoples R China
[3] Yangzhou Univ, Dept Math, Jiangsu 225002, Peoples R China
[4] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Commun Syst & Networks CSN Res Grp, Jeddah, Saudi Arabia
基金
中国国家自然科学基金;
关键词
Dynamic event-triggered mechanism; Encoding-decoding scheme; Spatial -temporal networks; State estimation; Time-delay; PARTIAL-DIFFERENTIAL SYSTEMS; NEURAL-NETWORKS; COMPLEX NETWORKS; SENSOR NETWORKS; ROBUST FILTER; SET;
D O I
10.1016/j.neucom.2022.05.062
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper is concerned with the dynamic event-triggered state estimation problem for a class of spatial -temporal networks (STNs) with time-varying delays under an encoding-decoding strategy. For the sake of reducing the unnecessary resources wastes, we establish a dynamic event-triggered mechanism to deter-mine whether the current measurement output data is transmitted to the filter, where the threshold is dynamically adjusted according to a certain rule. In order to enhance the robustness of signal transmis-sion, an encoding-decoding strategy is exploited in the process of the data transmission. To be specific, the original signals encoded as a bit string are transmitted through binary symmetric channels with cer-tain crossover probabilities and then restored by a decoder at the receiver. By constructing Lyapunov-Krasovskii functional, we obtain a sufficient condition to ensure that the estimation error system is expo-nential mean square ultimately bounded. Subsequently, the desired state estimator is designed in terms of the solution to a certain matrix inequality. Finally, a numerical example is shown to demonstrate that the proposed state estimator is valid for time-delayed STNs. (c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页码:868 / 876
页数:9
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