Moving horizon estimation with non-uniform sampling under component-based dynamic event-triggered transmission

被引：162

作者：

Zou, Lei ^{[1
]}

Wang, Zidong ^{[1
,2
]}

Zhou, Donghua ^{[2
]}

机构：

[1] Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England

[2] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China

来源：

AUTOMATICA | 2020年 / 120卷

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Moving horizon estimation; Non-uniform sampled data; Component-based dynamic event-triggered transmission; Ultimate boundedness; TIME-VARYING SYSTEMS; STATE ESTIMATION; NONLINEAR-SYSTEMS; COMMUNICATION; STABILIZATION; CONSENSUS; DESIGN; FILTER; DELAY;

D O I：

10.1016/j.automatica.2020.109154

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 concerned with the moving horizon (MH) estimation problem for linear systems with non-uniform sampling under component-based dynamic event-triggered transmission (DETT) scheme. The sampling interval is allowed to be time-varying with known upper and lower bounds. A so-called component-based DETT scheme is adopted to determine the transmission instant at which an individual sensor sends the measurement to the estimator through certain communication network. Then, a time-varying MH estimator is designed with the corresponding estimation error dynamics characterized by a linear discrete time-varying system whose time-varying parts induced by the non-uniform sampling are modeled by certain norm bounded uncertainties. Sufficient conditions are derived to guarantee the ultimate boundedness of the estimation error. Moreover, within the established theoretical framework, the desired estimator parameter is calculated by solving a set of linear matrix inequalities. Finally, a simulation example is given to illustrate the effectiveness of our proposed MH estimation method. (C) 2020 Elsevier Ltd. All rights reserved.

引用

页数：13

共 55 条

[1] Receding-horizon estimation for discrete-time linear systems
Alessandri, A
Baglietto, M
Battistelli, G
[J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2003, 48 (03) : 473 - 478
[2] Moving-Horizon State Estimation for Nonlinear Systems Using Neural Networks
Alessandri, Angelo
Baglietto, Marco
Battistelli, Giorgio
Gaggero, Mauro
[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2011, 22 (05): : 768 - 780
[3] Mean-square H∞ filtering for stochastic systems: Application to a 2DOF helicopter
Basin, Michael
Elvira-Ceja, Santiago
Sanchez, Edgar N.
[J]. SIGNAL PROCESSING, 2012, 92 (03) : 801 - 806
[4] Optimal state estimation for networked systems with random parameter matrices, correlated noises and delayed measurements
Caballero-Aguila, R.
Hermoso-Carazo, A.
Linares-Perez, J.
[J]. INTERNATIONAL JOURNAL OF GENERAL SYSTEMS, 2015, 44 (02) : 142 - 154
[5] Event based sampling in non-linear filtering
Cea, Mauricio G.
Goodwin, Graham C.
[J]. CONTROL ENGINEERING PRACTICE, 2012, 20 (10) : 963 - 971
[6] Distributed Kalman filtering for time-varying discrete sequential systems
Chen, Bo
Hu, Guoqiang
Ho, Daniel W. C.
Yu, Li
[J]. AUTOMATICA, 2019, 99 : 228 - 236
[7] Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays
Chen, Dongyan
Yu, Yonglong
Xu, Long
Liu, Xiaohui
[J]. DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2015, 2015
[8] Neural-Network-Based Consensus Control for Multiagent Systems With Input Constraints: The Event-Triggered Case
Ding, Derui
Wang, Zidong
Han, Qing-Long
[J]. IEEE TRANSACTIONS ON CYBERNETICS, 2020, 50 (08) : 3719 - 3730
[9] Output-Based Event-Triggered Control With Guaranteed L∞-Gain and Improved and Decentralized Event-Triggering
Donkers, M. C. F.
Heemels, W. P. M. H.
[J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2012, 57 (06) : 1362 - 1376
[10] Distributed event-triggered control of multi-agent systems with combinational measurements
Fan, Yuan
Feng, Gang
Wang, Yong
Song, Cheng
[J]. AUTOMATICA, 2013, 49 (02) : 671 - 675

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