Distributed set-membership estimation for state-saturated systems with mixed time-delays via a dynamic event-triggered scheme

被引:7
|
作者
Fan, Sha [1 ]
Yan, Huaicheng [1 ,4 ]
Zhan, Xisheng [2 ]
Zhou, Ge [3 ]
Shi, Kaibo [4 ]
机构
[1] East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China
[2] Hubei Normal Univ, Coll Mechatron & Control Engn, Huangshi 435002, Hubei, Peoples R China
[3] Shanghai Electromech Engn Inst, Shanghai 201109, Peoples R China
[4] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China
基金
中国国家自然科学基金;
关键词
COMPLEX NETWORKS;
D O I
10.1016/j.jfranklin.2021.08.035
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper is focused on the distributed estimation issue in the form of set-membership (SM) for a class of discrete time-varying systems suffering mix-time-delays and state-saturations. The phenomena of time-delays and state-saturations are introduced to better describe insightful engineering. During local measurements transmission between sensors over a resource-limited sensor network, to prevent data collisions and resource-consumption, a newly dynamic event-triggering strategy (DETS) is designed to dispatch the local measurements transmission for each sensor to its neighbors. Compared with the most existing static ETSs, this DETs can mitigate the total number of triggering times and enlarge interval time between consecutive triggering instants. Then, some novel adequate criteria for designing the desired event-based SM estimators are derived such that the plant's true state always resides in each sensor's ellipsoidal region regardless of the simultaneous presence of bounded noises, mixed time delays and state-saturations. Subsequently, a recursive optimization algorithm is formulated such that the minimal ellipsoids, the estimators gains and event-triggering weighted matrices are acquired simultaneously. A verification simulation is presented to illustrate the advantages of the design approach of the developed state estimator. (c) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:10079 / 10094
页数:16
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