Quantised polynomial filtering for nonlinear systems with missing measurements

This paper is concerned with the polynomial filtering problem for a class of nonlinear systems with quantisations and missing measurements. The nonlinear functions are approximated with polynomials of a chosen degree and the approximation errors are described as low-order polynomial terms with norm-...

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Vydáno v:International journal of control Ročník 91; číslo 10; s. 2250 - 2260
Hlavní autoři: Liu, Yang, Wang, Zidong, Zhou, D. H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 03.10.2018
Taylor & Francis Ltd
Témata:
Algorithms
Approximation
Covariance
Filter design (mathematics)
Filtration
Functions (mathematics)
Mathematical analysis
missing measurements
Nonlinear filtering
Nonlinear systems
polynomial approximation
Polynomials
quantisation
recursive algorithm
Upper bounds
ISSN:0020-7179, 1366-5820
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Shrnutí:This paper is concerned with the polynomial filtering problem for a class of nonlinear systems with quantisations and missing measurements. The nonlinear functions are approximated with polynomials of a chosen degree and the approximation errors are described as low-order polynomial terms with norm-bounded coefficients. The transmitted outputs are quantised by a logarithmic quantiser and are also subject to randomly missing measurements governed by a Bernoulli distributed sequence taking values on 0 or 1. Dedicated efforts are made to derive an upper bound of the filtering error covariance in the simultaneous presence of the polynomial approximation errors, the quantisations as well as the missing measurements at each time instant. Such an upper bound is then minimised through designing a suitable filter gain by solving a set of matrix equations. The filter design algorithm is recursive and therefore applicable for online computation. An illustrative example is exploited to show the effectiveness of the proposed algorithm.
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ISSN:0020-7179
1366-5820
DOI:10.1080/00207179.2017.1337933