Kalman Filtering for Discrete-Time Networked Control Systems with Incomplete Observations: A Dropping Over-Delayed Packets Approach

Zhang, Yiming; Sircoulomb, Vincent; Langlois, Nicolas

doi:10.3182/20130204-3-fr-4031.00009

Cited by 6 publications

(3 citation statements)

References 14 publications

(11 reference statements)

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“…Because not every feedback output can be provided for the estimator, we have to use the received packet y r to estimate the state. In order to distinguish y r from the transmitted packet y k , a binary random variable γ k is introduced to denote packet arrival status as follows:

γ_{k} = ({, \begin{matrix} 1 & , & if packet arrives \\ 0 & , & otherwise \end{matrix})

where the expectation of γ is

E (γ) = \lim_{k \to + \infty} (γ_{k}) = λ

.…”

Section: Problem Formulationmentioning

confidence: 99%

Robust optimal estimation over networks: Application to battery state of charge estimation

Zhang¹,

Sircoulomb²,

Langlois³

2015

Intl J Robust & Nonlinear

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Summary In this paper, we provide a general framework for robust optimal estimation over a lossy and delayed network. A threshold principle is introduced to integrate network‐induced uncertainties into packet losses, which are modeled with a Bernoulli process. Based on stability conditions derived from two Riccati equations, we show the existence of critical observation arrival probabilities below which the optimal estimator stochastically fails to converge. Moreover, the result is extended to a real system with variable process disturbance, which has an indicator for its admissible bound in terms of a given restriction of estimation accuracy. The proposed method is experimented on a specific automobile application, the battery state of charge estimation. Copyright © 2015 John Wiley & Sons, Ltd.

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γ_{k} = ({, \begin{matrix} 1 & , & if packet arrives \\ 0 & , & otherwise \end{matrix})

where the expectation of γ is

E (γ) = \lim_{k \to + \infty} (γ_{k}) = λ

.…”

Section: Problem Formulationmentioning

confidence: 99%

Robust optimal estimation over networks: Application to battery state of charge estimation

Zhang¹,

Sircoulomb²,

Langlois³

2015

Intl J Robust & Nonlinear

Self Cite

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show abstract

“…13,14 The KF provides the best states estimate of the system based on noise measurements as well as actual values. 15,16 Network latency is the time delay between sending data at one point and receiving it at another point on the network. Network latency is difficult to avoid in real life as there are many factors that cause it.…”

Section: Introductionmentioning

confidence: 99%

“…13,14 The KF provides the best states estimate of the system based on noise measurements as well as actual values. 15,16…”

Section: Introductionmentioning

confidence: 99%

Event-based predictive control for interval type-2 Takagi–Sugeno fuzzy systems under Kalman filter

Wang,

Rong,

2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, the event-based predictive control problem for a class of interval type-2 Takagi–Sugeno fuzzy system is investigated. To reduce the influence of Gaussian noise, an improved Kalman filter is proposed, in which the system state is filtered or not depends on an event-triggered mechanism. Then, by designing the network predictive control scheme via interval type-2 fuzzy model, some sufficient conditions are derived, which guarantee the Schur stabilization of the concerned system subject to network delays. Finally, the correctness and effectiveness are verified by two simulation examples.

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Stealthy FDI attacks on modified Kalman filtering in complex networks with non-Gaussian-Lévy noise

Yuan,

Tong,

Dong

et al. 2024

Chaos, Solitons & Fractals

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Kalman Filtering for Discrete-Time Networked Control Systems with Incomplete Observations: A Dropping Over-Delayed Packets Approach

Cited by 6 publications

References 14 publications

Robust optimal estimation over networks: Application to battery state of charge estimation

Robust optimal estimation over networks: Application to battery state of charge estimation

Event-based predictive control for interval type-2 Takagi–Sugeno fuzzy systems under Kalman filter

Stealthy FDI attacks on modified Kalman filtering in complex networks with non-Gaussian-Lévy noise

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