H_inf Functional filtering for linear systems with unknown inputs

Mohamed Darouach, Tyrone Fernando

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


A new method for the H1 functional filtering for systems with unknown inputs is presented. First a functional filtering technique is presented, then we show that the dynamic of the estimation error can be modeled in the descriptor system form, this formulation makes it possible to describe the dynamic estimation error free of the derivative of the disturbances. The filter parameters are obtained from the bounded real lemma of descriptor systems. The developed approach unifies the design of functional, reduced-order and full-order filters. Necessary and sufficient conditions for the solvability of the problem are obtained in terms of a set of bilinear matrix inequalities (BMI). Under certain conditions these inequalities can be transformed to a set of linear matrix inequalities (LMI). Two numerical examples are presented to illustrate the approach described in the paper, the first example concerns the estimation of the state of charge (SOC) of the the lithium-ion battery and the second example highlights the reduced nature of the proposed functional filter.

Original languageEnglish
Pages (from-to)4858-4865
Number of pages8
JournalIEEE Transactions on Automatic Control
Issue number10
Early online date25 Nov 2020
Publication statusPublished - Oct 2021


Dive into the research topics of 'H_inf Functional filtering for linear systems with unknown inputs'. Together they form a unique fingerprint.

Cite this