State estimation for time‐delay systems with probabilistic sensor gain reductions

He, Xiao; Wang, Zidong; Zhou, Donghua

doi:10.1002/apj.190

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…Moreover, if (10) is true, the desired filter parameters are given by (11) where comes from the factorization of . Proof: The proof is similar with the treatment in [7], and is therefore omitted here for the limitation of space.…”

Section: Resultsmentioning

confidence: 98%

Robust $H_{\infty}$ Filtering for Time-Delay Systems With Probabilistic Sensor Faults

Wang

Zhou

2009

IEEE Signal Process. Lett.

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Abstract-In this paper, a new robust filtering problem is investigated for a class of time-varying nonlinear system with norm-bounded parameter uncertainties, bounded state delay, sector-bounded nonlinearity and probabilistic sensor gain faults. The probabilistic sensor reductions are modeled by using a random variable that obeys a specific distribution in a known interval [ ], which accounts for the following two phenomenon: 1) signal stochastic attenuation in unreliable analog channel and 2) random sensor gain reduction in severe environment. The main task is to design a robust filter such that, for all possible uncertain measurements, system parameter uncertainties, nonlinearity as well as time-varying delays, the filtering error dynamics is asymptotically mean-square stable with a prescribed performance level. A sufficient condition for the existence of such a filter is presented in terms of the feasibility of a certain linear matrix inequality (LMI). A numerical example is introduced to illustrate the effectiveness and applicability of the proposed methodology.Index Terms-Linear matrix inequality (LMI), parameter uncertainties, robust filtering, sensor gain reduction.

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Section: Resultsmentioning

confidence: 98%

Robust $H_{\infty}$ Filtering for Time-Delay Systems With Probabilistic Sensor Faults

Wang

Zhou

2009

IEEE Signal Process. Lett.

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“…In addition to the common sensor failure phenomenon (Peng et al, 2018;B. Zhang et al, 2020), sensor gain degradation (He et al, 2008;Y. Liu et al, 2014 also occurs.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al (2014) for networked systems with stochastic sensor gain degradation. By using the linear matrix inequality method, a new state estimation method was presented in He et al (2008) for timedelay systems with probabilistic sensor gain degradation. The minimum variance filtering problem was discussed in Y.…”

Section: Introductionmentioning

confidence: 99%

Fault estimation for nonlinear systems with sensor gain degradation and stochastic protocol based on strong tracking filtering

Sheng

Gao

2020

Systems Science & Control Engineering

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This paper focuses on the fault estimation problem for a class of nonlinear systems with sensor gain degradation and stochastic protocol (SP) based on strong tracking filtering. The phenomenon of the sensor gain degradation is described by sequences of stochastic variables in a known interval. The stochastic protocol (SP) is used to deal with possible data conflicts in multi-signal transmission. The augmented system is constructed by combining the original system state vectors and the related faults into an augmented state vectors. The strong tracking filter (STF) is designed by introducing a fading factor into the filter structure to solve the problem of burst faults. Finally, a simulation example is given to verify the effectiveness and applicability of the proposed filter.

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“…This is particularly true for systems which experience unsteady or abnormal working conditions [19], [20], [25], [28], [31], for example, intermittent sensor outages, sensor aging or transmission congestions in networked environments. Note that the filtering problem for systems whose sensor gains are subject to random degradation has received some initial research attention [9]. Unfortunately, despite its practical significance, the filtering problem with stochastic sensor gain degradation over sensor networks has not been investigated yet for timevarying systems due mainly to the mathematical difficulties, not to mention the case where the filter performance becomes a concern in the design.…”

Section: Introductionmentioning

confidence: 99%

Minimum-Variance Recursive Filtering Over Sensor Networks With Stochastic Sensor Gain Degradation: Algorithms and Performance Analysis

Liu

Wang

et al. 2016

IEEE Trans. Control Netw. Syst.

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This paper is concerned with the minimum variance filtering problem for a class of time-varying systems with both additive and multiplicative stochastic noises through a sensor network with a given topology. The measurements collected via the sensor network are subject to stochastic sensor gain degradation, and the gain degradation phenomenon for each individual sensor occurs in a random way governed by a random variable distributed over the interval [0, 1]. The purpose of the addressed problem is to design a distributed filter for each sensor such that the overall estimation error variance is minimized at each time step via a novel recursive algorithm. By solving a set of Riccati-like matrix equations, the parameters of the desired filters are calculated recursively. The performance of the designed filters is analyzed in terms of both the boundedness and monotonicity. Specifically, sufficient conditions are obtained under which the estimation error is exponentially bounded in mean square. Moreover, the monotonicity property for the error variance with respect to the sensor gain degradation is thoroughly discussed. Numerical simulations are exploited to illustrate the effectiveness of the proposed filtering algorithm and the performance of the developed filter.

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State estimation for time‐delay systems with probabilistic sensor gain reductions

Cited by 6 publications

References 17 publications

Robust $H_{\infty}$ Filtering for Time-Delay Systems With Probabilistic Sensor Faults

Robust $H_{\infty}$ Filtering for Time-Delay Systems With Probabilistic Sensor Faults

Fault estimation for nonlinear systems with sensor gain degradation and stochastic protocol based on strong tracking filtering

Minimum-Variance Recursive Filtering Over Sensor Networks With Stochastic Sensor Gain Degradation: Algorithms and Performance Analysis

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