文献类型：期刊文献

英文题名：Event-triggered minimax state estimation with a relative entropy constraint

作者：Xu, Jiapeng[1];Tang, Yang[1];Yang, Wen[1];Li, Fangfei[2];Shi, Ling[3]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China;[3]Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China

年份：2019

卷号：110

外文期刊名：AUTOMATICA

收录：;EI(收录号：20194007489824);WOS:【SCI-EXPANDED(收录号:WOS:000495491900006)】；

基金：This work was supported by the National Key Research and Development Program of China under Grant 2018YFC0809302, the National Natural Science Foundation of China (Grant Nos. 61751305, 61673176, 61973123, 61573143, 61773161), the Hong Kong RGC General Research Fund 16204218, the Science and Technology Commission of Shanghai Municipality under Grant 18ZR1409800, and the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017. The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Michele Basseville under the direction of Editor Torsten Soderstrom.

语种：英文

外文关键词：Event-triggered state estimation; Minimax estimation; Robustness; Relative entropy constraint

摘要：In this paper, we consider an event-triggered minimax state estimation problem for uncertain systems subject to a relative entropy constraint. This minimax estimation problem is formulated as an equivalent event-triggered linear exponential quadratic Gaussian problem. It is then shown that this problem can be solved via dynamic programming and a newly defined information state. As the solution to this dynamic programming problem is computationally intractable, a one-step event-triggered minimax estimation problem is further formulated and solved, where an a posteriori relative entropy is introduced as a measure of the discrepancy between probability measures. The resulting estimator is shown to evolve in recursive closed-form expressions. For the multi-sensor system scenario, a one-step event-triggered minimax estimator is also presented in a sequential fusion way. Finally, comparative simulation examples are provided to illustrate the performance of the proposed one-step event-triggered minimax estimators. (C) 2019 Elsevier Ltd. All rights reserved.