Robust ℋ∞ filter design for singular systems with time-varying uncertainties

Barbosa, Karina A.; Cipriano, Aldo

doi:10.1049/iet-cta.2010.0175

Cited by 14 publications

(12 citation statements)

References 21 publications

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“…While a full-scale review over the field is clearly out of the scope of this paper, interested readers are referred to (an incomplete list of) [3][4][5][6][7][8][9][10][11][12][13][14][15][16], for the development of this subject in several directions over the past 10 years. The multiplier approach to robust estimation has been considered in [17][18][19][20][21]; none of these allows the use of dynamic IQCs.…”

Section: Introductionmentioning

confidence: 99%

Robust estimation with dynamic integral quadratic constraints: the discrete‐time case

Kao¹,

Chen²

2013

IET Control Theory & Applications

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The authors consider the problem of robust output estimation in the discrete-time setting. The uncertainty in the system is characterised by integral quadratic constraints with dynamic multipliers. The main technical contribution of this study is to derive two different synthesis conditions in terms of linear matrix inequalities (LMI) by two different approaches. The LMI condition derived by the first approach allows one to find the robust estimator and the performance certificate simultaneously, whereas the condition by the second approach has a smaller size but requires an additional step for recovering the estimator. The synthesis conditions are validated by numerical examples. The results verify the equivalence of the two approaches and show that the two-step approach is in fact computationally favourable when the system has large number of state variables. NomenclatureSymbols R, R n , R n×m and Z + are used to denote the sets of real numbers, n-dimensional real vectors, n × m real matrices and non-negative integers, respectively. Symbols I n and 0 n×m are used to denote n-dimensional identity matrix and n × m zero matrix, respectively. The subscripts are dropped when the dimension is evident from the text. Given a matrix M , the transposition and the conjugate transposition are denoted by M and M * , respectively. Given matrix M and N , we use diag(M , N ) to denote the block-diagonal matrix with M and N being the (1,1) block and the (2,2) block, respectively.

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Section: Introductionmentioning

confidence: 99%

Robust estimation with dynamic integral quadratic constraints: the discrete‐time case

Kao¹,

Chen²

2013

IET Control Theory & Applications

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“…Noting that time delays and parameter uncertainties arise quite naturally and are frequently encountered in a variety of practical systems as well as singular systems [3,[11][12][13]17,30], which often lead to enormously deteriorated performance or even instability of the given systems. Due to a given singular system must be not only stable but also regular and impulse free or causal at the same time, the investigation for singular time delay systems, especially singular time-varying delay systems, is much more complicated than that for state-space systems [14,16,[18][19][20][21]24].…”

Section: Introductionmentioning

confidence: 99%

“…It is well known that H 1 filtering is an important state estimation method, which can estimate the unavailable state variables without the statistical information of the external noises [3,4,13,17,18,39]. In recent years, H 1 filtering for SMJSs has been extensively studied, for example, [38,40], and the references therein.…”

Section: Introductionmentioning

confidence: 99%

H_∞ deconvolution filter design for uncertain descriptor hybrid delay systems with limited communication capacity and unknown transition rates

et al. 2016

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This article investigates the problem of H 1 deconvolution filtering for a kind of uncertain descriptor hybrid system with state time-varying delays, limited communication capacity, and partially unknown transition rates. The limited communication capacity including signal transmission delays and data packet dropout. Mode-dependent and delay-dependent conditions are obtained by constructing a comprehensive stochastic Lyapunov-Krasovskii functional, which ensure the filtering error system is not only stochastically admissible but also satisfies a prescribed H 1 -norm level for all admissible uncertainties, signal transmission delays, and data packet dropout. The linear matrix inequalities technique is utilized to acquire the desired deconvolution filter parameters. An electrical RLC circuit example and two numerical examples are provided to demonstrate the usefulness and effectiveness of our methods.

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“…As is well known, H ∞ filtering is an important method to estimate the unavailable state variables of a given system through noisy measurements. In the past years, the study of the H ∞ filtering problem has attracted a great deal of attention and has been extensively applied to many control systems including singular systems and singular Markovian jump systems (see, e.g., and the references therein).…”

Section: Introductionmentioning

confidence: 99%

Robust H_∞ deconvolution filtering for uncertain singular Markovian jump systems with time‐varying delays

Zhuang

Zhang

et al. 2015

Intl J Robust & Nonlinear

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SUMMARYThe problem of H 1 deconvolution filter design for a class of singular Markovian jump systems with time-varying delays and parameter uncertainties is considered in this paper. By constructing a more comprehensive stochastic Lyapunov-Krasovskii functional, novel delay-dependent conditions are established to guarantee the filtering error system is not only stochastically admissible, but also satisfies a prescribed H 1 -norm level for all admissible uncertainties. The desired filter parameters can be obtained by solving a set of strict linear matrix inequalities. Two examples and an electrical RLC circuit example are employed to verify the effectiveness and usefulness of the proposed methods in the paper.

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Robust ℋ∞ filter design for singular systems with time-varying uncertainties

Cited by 14 publications

References 21 publications

Robust estimation with dynamic integral quadratic constraints: the discrete‐time case

Robust estimation with dynamic integral quadratic constraints: the discrete‐time case

H_∞ deconvolution filter design for uncertain descriptor hybrid delay systems with limited communication capacity and unknown transition rates

Robust H_∞ deconvolution filtering for uncertain singular Markovian jump systems with time‐varying delays

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Robust ℋ∞ filter design for singular systems with time-varying uncertainties

Cited by 14 publications

References 21 publications

Robust estimation with dynamic integral quadratic constraints: the discrete‐time case

Robust estimation with dynamic integral quadratic constraints: the discrete‐time case

H∞ deconvolution filter design for uncertain descriptor hybrid delay systems with limited communication capacity and unknown transition rates

Robust H∞ deconvolution filtering for uncertain singular Markovian jump systems with time‐varying delays

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H_∞ deconvolution filter design for uncertain descriptor hybrid delay systems with limited communication capacity and unknown transition rates

Robust H_∞ deconvolution filtering for uncertain singular Markovian jump systems with time‐varying delays