Reduced order H/sub ∞/ filters for discrete linear systems

Rawson, Jenny L.; Hsu, C.S.

doi:10.1109/cdc.1997.652356

Cited by 11 publications

(12 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From a technical point of view, there are generally two approaches to designing H ∞ filters, i.e. the Riccati difference equation (RDE) approach 1–7 and the linear matrix inequality (LMI) approach 8–13.…”

Section: Introductionmentioning

confidence: 99%

“…With respect to the RDE approach, a bounded real lemma (BRL) has been presented in 1 based on a backward recursive RDE for deterministic systems, and its stochastic counterpart has been established in 2. Since the backward RDE is only suitable for offline computation, another version of BRL has been proposed by means of the forward recursive RDE using Hilbert adjoints for deterministic systems 3, 4 and stochastic systems 5 to facilitate the online application. On the other hand, due to its computational efficiency, the LMI approach 14–18 has recently received particular research interests in designing H ∞ filters 8, 9.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

H_∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

Shen

Wang

Shu

et al. 2010

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

SUMMARYThis paper is concerned with the robust H ∞ finite-horizon filtering problem for discrete time-varying stochastic systems with multiple randomly occurred sector-nonlinearities (MROSNs) and successive packet dropouts. MROSNs are proposed to model a class of sector-like nonlinearities that occur according to the multiple Bernoulli distributed white sequences with a known conditional probability. Different from traditional approaches, in this paper, a time-varying filter is designed directly for the addressed system without resorting to the augmentation of system states and measurement, which helps reduce the filter order. A new H ∞ filtering technique is developed by means of a set of recursive linear matrix inequalities that depend on not only the current available state estimate but also the previous measurement, therefore ensuring a better accuracy. Finally, two illustrative examples are used to demonstrate the effectiveness and applicability of the proposed filter design scheme.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

H_∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

Shen

Wang

Shu

et al. 2010

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…For example, the bounded real lemma (BRL) was derived in [28] based on the Riccati difference equation, which is suitable for offline computation since the boundary condition are given for the end of the known interval. In [12], by employing the method of Hilbert adjoins, another version of BRL was obtained, based on which the Riccati difference equation can be solved forward in time, and a reduced order H ∞ filter was designed for the linear discrete-time system. In [9], a robust H ∞ filter with error variance constraints was designed for discrete time-varying uncertain system by forwardly solving a recursive Riccati difference equation.…”

Section: Introductionmentioning

confidence: 99%

Robust H∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects

et al. 2010

View full text Add to dashboard Cite

In this paper, the robust H∞ finite-horizon filtering problem is investigated for discrete time-varying stochastic systems with polytopic uncertainties, randomly occurred nonlinearities as well as quantization effects. The randomly occurred nonlinearity, which describes the phenomena of a nonlinear disturbance appearing in a random way, is modeled by a Bernoulli distributed white sequence with a known conditional probability. A new robust H∞ filtering technique is developed for the addressed Itô-type discrete time-varying stochastic systems. Such a technique relies on the forward solution to a set of recursive linear matrix inequalities and is therefore suitable for on-line computation. It is worth mentioning that, in the filtering process, the information of both the current measurement and the previous state estimate is employed to estimate the current state. Finally, a simulation example is exploited to show the effectiveness of the method proposed in this paper.

show abstract

“…Then, H ∞ filter as an alternative approach is proposed [3], [4], [5]. Moreover, considering some practical problems, like high requirement to facility, the reduced-order H ∞ filtering becomes a new approach to deal with the filtering problem, and many results on this issue have been obtained [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it can be easily deduced from definition 1 that the pair (E, A) is regular and impulse-free.Using the method stated in remark 5 to solve the inequalities in (29)-(32), when γ=0 6,. we obtain the solution as follows(for space consideration, we just give the solution of X) easy to find rankX=1.…”

mentioning

confidence: 99%

Reduced-order H<inf>∞</inf> filtering for singular systems with network delays

Xue

et al. 2009

2009 4th IEEE Conference on Industrial Electronics and Applications

View full text Add to dashboard Cite

This paper deals with the problem of reducedorder H ∞ filtering for singular systems with network delays. Such delays include both signal transmission delay and data packet dropout. A sufficient condition of the delay dependent H ∞ performance is obtained in terms of matrix inequalities (MIS) to ensure the filtering error singular system is regular, impulsefree, stable and satisfies a prescribed H ∞ performance level. By using the projection theorem, these MIS are transformed into linear matrix inequalities (LMIS) and a linear filter that has specified order lower than the given system is designed. A special case called static or zeroth-order H ∞ filter is appeared when the order of the filter reduced to zero. Finally, a numerical example is introduced to illustrate the effectiveness of the proposed approach.Index Terms-Reduced-order H ∞ filter, singular system, network delay, linear matrix inequality(LMI).

show abstract

Reduced order H/sub ∞/ filters for discrete linear systems

Cited by 11 publications

References 2 publications

H_∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

H_∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

Robust H∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects

Reduced-order H<inf>∞</inf> filtering for singular systems with network delays

Contact Info

Product

Resources

About

Reduced order H/sub ∞/ filters for discrete linear systems

Cited by 11 publications

References 2 publications

H∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

H∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

Robust H∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects

Reduced-order H<inf>&#x221E;</inf> filtering for singular systems with network delays

Contact Info

Product

Resources

About

H_∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

H_∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

Reduced-order H<inf>∞</inf> filtering for singular systems with network delays