filtering for T–S fuzzy complex networks subject to sensor saturation via delayed information

Sheng, Suying; Zhang, Xiaomei

doi:10.1049/iet-cta.2017.0071

Cited by 13 publications

(1 citation statement)

References 40 publications

(38 reference statements)

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“…Over the past few decades, solving filtering problems has received significant attention with the rapid development of industrialization and digitization. A host of excellent filtering algorithms have been proposed by many researchers, such as the Kalman filtering [1], the extended Kalman filtering (EKF) [2,3], the H ∞ filtering [4,5], and the fusion filtering [6,7]. The classical Kalman filter has been widely employed in practice, particularly in engineering fields, because of its high filtering accuracy.…”

Section: Introductionmentioning

confidence: 99%

Recursive filtering for nonlinear systems subject to measurement outliers

Jiang

Gao

Han

et al. 2021

Sci. China Inf. Sci.

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In this paper, an innovative recursive filtering algorithm (RFA) is proposed for a class of nonlinear systems (NSs) subject to multiplicative noises (MNs) and measurement outliers (MOs). Initially, the MNs are employed to formulate the random influence on the NSs with the stochastic noises. Next, the outlier phenomenon could occur unpredictably during measurement transmission. Then, a self-adaptive saturation function is introduced to the constructed filter to mitigate the influence of MOs on the filter performance. In this paper, we design a resistant-outlier filter for NSs with MNs and MOs, and the filter gain ensures that the trace of the filtering error covariance matrix is minimized by solving the constructed Riccati-like difference equations. Moreover, the exponential boundedness of the filtering error in the sense of mean square is analyzed. Finally, the feasibility of the proposed RFA is illustrated by a simulation example when the MOs occur.

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Section: Introductionmentioning

confidence: 99%

Recursive filtering for nonlinear systems subject to measurement outliers

Jiang

Gao

Han

et al. 2021

Sci. China Inf. Sci.

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Event‐triggered filtering and intermittent fault detection for time‐varying systems with stochastic parameter uncertainty and sensor saturation

Zhang

Christofides

et al. 2018

Intl J Robust & Nonlinear

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Summary In this paper, the event‐triggered filtering and intermittent fault detection problems are investigated for a class of time‐varying systems with stochastic parameter uncertainty and sensor saturation. Due to the existence of event‐triggered mechanism, the measured signal could be transmitted only when it satisfies the triggering condition. An event‐triggered filter is developed, which takes the event‐triggered mechanism, parameter uncertainty, and sensor saturation into full consideration but does not depend on any specific uncertainty structure. By utilizing the inductive and stochastic analysis technique, the filter gain is designed to ensure that the upper bound of the estimation error covariance is minimized at each time step. Based on the proposed filter, a residual is generated and the corresponding evaluation function and detection threshold are given to achieve fault detection. At last, two simulation studies are carried out to demonstrate the effectiveness and applicability of the proposed method.

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H_∞ finite‐horizon filtering for complex networks with state saturations: The weighted try‐once‐discard protocol

Wang

Shen

et al. 2019

Intl J Robust & Nonlinear

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Summary This paper addresses the finite‐horizon H∞ filtering problem for a kind of discrete state‐saturated time‐varying complex networks subjected to the weighted try‐once‐discard (WTOD) protocol. Under the WTOD protocol, only the measurement signal from one sensor node is allowed to be transmitted to the filter at each time point, where such a node is selected based on a certain quadratic selection principle. The main purpose of this paper is to design an H∞ filter that guarantees the disturbance attenuation level on a given finite time‐horizon for the underlying complex network subject to both state saturations and WTOD protocols. By using the convex hull approach, sufficient conditions are first obtained to ensure the existence for the desired filter to achieve the H∞ performance specification by means of a few recursive matrix inequalities. Then, based on the obtained results, the filter parameters are designed, which cope effectively with both state saturations and communication protocols. Finally, a numerical simulation is employed to demonstrate the validity of the developed filter algorithm.

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filtering for T–S fuzzy complex networks subject to sensor saturation via delayed information

Cited by 13 publications

References 40 publications

Recursive filtering for nonlinear systems subject to measurement outliers

Recursive filtering for nonlinear systems subject to measurement outliers

Event‐triggered filtering and intermittent fault detection for time‐varying systems with stochastic parameter uncertainty and sensor saturation

H_∞ finite‐horizon filtering for complex networks with state saturations: The weighted try‐once‐discard protocol

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filtering for T–S fuzzy complex networks subject to sensor saturation via delayed information

Cited by 13 publications

References 40 publications

Recursive filtering for nonlinear systems subject to measurement outliers

Recursive filtering for nonlinear systems subject to measurement outliers

Event‐triggered filtering and intermittent fault detection for time‐varying systems with stochastic parameter uncertainty and sensor saturation

H∞ finite‐horizon filtering for complex networks with state saturations: The weighted try‐once‐discard protocol

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H_∞ finite‐horizon filtering for complex networks with state saturations: The weighted try‐once‐discard protocol