Reduced order filtering in an H<sup>∞</sup> setting

Kim, Hansil; Sims, Craig S.; Nagpal, K.M.

doi:10.23919/acc.1992.4792438

Cited by 11 publications

(2 citation statements)

References 3 publications

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“…The problem of H 1 filtering has been widely studied in the full order case (Shaked 1990, Nagpal and Khargonekar 1991, Shaked and Theodor 1992 and even in the reduced order one (Kim et al 1992, Grigoriadis and Watson 1997, Watson and Grigoriadis 1998. The advantage of the H 1 filtering is that it does not require any knowledge on the statiscal properties of the disturbances, they must only be on finite energy.…”

Section: Introductionmentioning

confidence: 99%

“…IQC are shown to describe these types of uncertainties in numerous papers well, notably for signal processing applications (Xie et al 1998). Due to computational or real-time implementation reasons, reduced order filter should be prefered to the full order one, specially in practical applications for large scale systems (Nagpal et al 1987, Kim et al 1992, Watson and Grigoriadis 1998.…”

Section: Introductionmentioning

confidence: 99%

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Robust reduced order unbiased filtering for uncertain systems

Zasadzinski

Ali

Darouach

2006

International Journal of Control

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This paper presents a simple solution to the robust H 1 unbiased functional reduced order filtering problem in the case of uncertain systems described by Integral Quadratic Constraint (IQC). The existence condition of the unbiased filter is expressed through a rank relation. Then the filter is designed via Linear Matrix Inequalities coupled with an equality constraint. The results obtained in this paper are applicable to unstable systems. The approach is illustrated by a numerical example.

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