Open AccessRobust set‐membership state estimator against outliers in data
Author(s) -
Meslem Nacim,
Hably Ahmad
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2019.0004
Subject(s) - outlier , estimator , robustness (evolution) , state estimator , robust statistics , computer science , algorithm , computation , state (computer science) , mathematics , control theory (sociology) , mathematical optimization , artificial intelligence , statistics , biochemistry , chemistry , control (management) , gene
Based on interval computation, a set‐membership state estimator capable to manage a certain type of outliers in measurements is proposed for uncertain discrete‐time linear systems. To achieve this purpose, two set‐valued filtering techniques are implemented in the presented state estimation algorithm. The setting up of these techniques offers two main advantages. On one hand, the convergence of the estimated state enclosures width is guaranteed and, on the other hand, the algorithm robustness against outliers in data is ensured. That is, unlike former methods, the proposed set‐valued state estimator preserves the framing property despite the presence of some false values in available sensors data. To show the efficiency and the performance of the introduced set‐valued state estimator, it is compared, through two numerical examples, with an optimal interval observer selected from the literature for its high performance.