Set-Membership Filter for Discrete-Time Nonlinear Systems Using State-Dependent Coefficient Parameterization

Bhattacharjee, Diganta; Subbarao, Kamesh

doi:10.1109/tac.2021.3082504

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…where 𝜃 is integral dummy variable and J(x i k ) is not unique. 39 Proof. Since f (0) = 0, we can use the fundamental theorem of integral calculus the identity…”

Section: Resilient Set-membership Filtermentioning

confidence: 99%

Partial‐node‐based resilient set‐membership filtering for output‐coupled complex networks with event‐triggered mechanisms

Lin,

Shen,

Liu

et al. 2024

Intl J Robust & Nonlinear

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This paper studies the resilient set‐membership filtering for discrete‐time nonlinear complex networks with output‐coupled topologies. An event‐triggered mechanism (ETM) is employed to save communication resources from the sensor to the filter. A resilient set‐membership filter (RSMF) handles unknown bounded uncertainty, resists the gain perturbation and makes the estimation errors to a certain ellipsoid region. Sufficient conditions for the existence of the RSMF are derived using mathematical induction techniques. The filter gain can be determined on the basis of solving a specific set of recursive matrix inequalities. Then, by optimizing the constrained ellipsoid of the estimation error, the optimized filtering performance is ensured. Finally, a numerical example is given to demonstrate the effectiveness of the proposed RSMF.

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“…where 𝜃 is integral dummy variable and J(x i k ) is not unique. 39 Proof. Since f (0) = 0, we can use the fundamental theorem of integral calculus the identity…”

Section: Resilient Set-membership Filtermentioning

confidence: 99%

Partial‐node‐based resilient set‐membership filtering for output‐coupled complex networks with event‐triggered mechanisms

Lin,

Shen,

Liu

et al. 2024

Intl J Robust & Nonlinear

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show abstract

“…Following are the corrections to [1]. 1) "Assumption 2" below the statement of Assumption 3.2 should read "Assumption 3.2."…”

Section: List Of Correctionsmentioning

confidence: 99%

“…

Some of the final sets of corrections requested in the proof did not unfortunately make it to the final version in [1]. We would like to point those out in this document.

…”

mentioning

confidence: 99%

Corrections to “Set-Membership Filter for Discrete-Time Nonlinear Systems Using State-Dependent Coefficient Parameterization” [Feb 22 894-901]

Bhattacharjee

Subbarao

2022

IEEE Trans. Automat. Contr.

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Some of the final sets of corrections requested in the proof did not unfortunately make it to the final version in [1]. We would like to point those out in this document. I. LIST OF CORRECTIONSFollowing are the corrections to [1]. 1) "Assumption 2" below the statement of Assumption 3.2 should read "Assumption 3.2." 2) "Assumption 1" below (9) should read "Assumption 3.1."3) The first sentence of Theorem 1 should read "Consider system (4) under Assumptions 3.1 and 3.2." 4) The first sentence of Theorem 2 should read "Consider system (4) under Assumption 3.2 with the...." 5) "Assumption 2" in Remark 3 should read "Assumption 3.2." 6) "Assumption 2" below (33) should read "Assumption 3.2."

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“…Fortunately, the so‐called set‐membership filtering (see refs. [29–31, 35, 36]) provides a rather promising countermeasure to this difficulty. Noise is assumed to be distributed in an unknown but bounded region in this algorithm, and the bound of the noise is easy to obtain relative to statistical properties.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…In refs. [35] and [36], linear functions are transformed into linear forms based on the state‐dependent coefficient parameterization and the semi‐infinite programming respectively. In ref.…”

Section: Introductionmentioning

confidence: 99%

Received signal strength indicator‐based set‐membership filtering indoor localization under multipath effect

Yang

Dong

Shang

et al. 2022

IET Control Theory & Appl

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The indoor moving target localization based on the received signal strength indicator is studied in this paper. By introducing filtering algorithms, a novel dynamic parameters setmembership filtering (DPSMF) is proposed to reduce the interference of the multipath effect and noises. First, the higher-order remainder is included in the bounded range based on the interval mathematics method to improve the linearization accuracy of the Taylor series expansion of the measurement function. Second, the parameters to be determined in the channel model are set to local dynamic parameters, which are obtained by solving a particular semi-definite programming problem. Third, the optimized localization estimations of the target node at each sampling instant and the ellipsoid set containing the real state of the system are obtained based on the DPSMF. Finally, an example is given to verify the effectiveness of the proposed DPSMF algorithm.

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Set-Membership Filter for Discrete-Time Nonlinear Systems Using State-Dependent Coefficient Parameterization

Cited by 10 publications

References 30 publications

Partial‐node‐based resilient set‐membership filtering for output‐coupled complex networks with event‐triggered mechanisms

Partial‐node‐based resilient set‐membership filtering for output‐coupled complex networks with event‐triggered mechanisms

Corrections to “Set-Membership Filter for Discrete-Time Nonlinear Systems Using State-Dependent Coefficient Parameterization” [Feb 22 894-901]

Received signal strength indicator‐based set‐membership filtering indoor localization under multipath effect

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