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Recursive state estimation for discrete‐time nonlinear systems with event‐triggered data transmission, norm‐bounded uncertainties and multiple missing measurements
Author(s) -
Zheng Xiujuan,
Fang Huajing
Publication year - 2016
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3527
Subject(s) - bounded function , mathematics , norm (philosophy) , filtering problem , nonlinear system , control theory (sociology) , recursive filter , filter (signal processing) , discrete time and continuous time , missing data , bernoulli's principle , riccati equation , computer science , mathematical optimization , algorithm , statistics , filter design , partial differential equation , mathematical analysis , physics , control (management) , engineering , quantum mechanics , root raised cosine filter , artificial intelligence , political science , law , computer vision , aerospace engineering
Summary In this paper, we consider the recursive state estimation problem for a class of discrete‐time nonlinear systems with event‐triggered data transmission, norm‐bounded uncertainties, and multiple missing measurements. The phenomenon of event‐triggered communication mechanism occurs only when the specified event‐triggering condition is violated, which leads to a reduction in the number of excessive signal transmissions in a network. A sequence of independent Bernoulli random variables is employed to model the multiple measurements missing in the transmission. The norm‐bounded uncertainties that could be considered as external disturbances which lie in a bounded set. The purpose of the addressed filtering problem is to obtain an optimal robust recursive filter in the minimum‐variance sense such that with the simultaneous presence of event‐triggered data transmission, norm‐bounded uncertainties, and multiple missing measurements; the filtering error is minimized at each sampling time. By solving two Riccati‐like difference equations, the filter gain is calculated recursively. Based on the stochastic analysis theory, it is proved that the estimation error is bounded under certain conditions. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed algorithm. Copyright © 2016 John Wiley & Sons, Ltd.