Open AccessUpper bound filter under interval constraints and multiplicative noises
Author(s) -
Yang Yanting,
Liang Yan,
Xu Linfeng,
Yang Yanbo,
Qin Yuemei
Publication year - 2019
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.2019.0253
Subject(s) - mathematics , multiplicative function , upper and lower bounds , covariance , scalar (mathematics) , control theory (sociology) , interval (graph theory) , filter (signal processing) , diagonal matrix , covariance matrix , diagonal , series (stratigraphy) , recursion (computer science) , recursive filter , mathematical optimization , algorithm , filter design , computer science , statistics , mathematical analysis , combinatorics , root raised cosine filter , paleontology , geometry , control (management) , artificial intelligence , computer vision , biology
The filtering problem is considered for dynamic systems perturbed by additive and multiplicative noises under interval constraints. Interval constraints induce unknown inputs existing in the reconstructed dynamical model when transforming the state constrained problem into the unconstrained one, which makes the filter design based on minimising the estimate error covariance fail. Due to the coexistence of multiplicative noises and unknown inputs caused by interval uncertainty, a novel recursive upper bound filtering structure is designed for the considered system by a series of linear matrix inequalities. Then, the recursion of the upper bound of the estimate error covariance is realised through the scalar parameter optimisation, based on scaling the diagonal sub‐block matrices and non‐diagonal sub‐block matrices of the innovation covariance which is dependent on the arriving measurement adaptively. Finally, a numerical example shows the effectiveness of the proposed method.