Optimal linear estimators for systems with random measurement delays

Sun, Shuli; Tian, Tian

doi:10.1007/s11768-011-0244-7

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…Models and can simultaneously describe consecutive packet dropouts and possible one‐step, two‐step, up to d 1 ‐step, and d 2 ‐step random transmission delays in networked systems. They are more general than those proposed in where the multiple packet dropouts or random time delays are considered, respectively. Take as an example, Table shows the data transmission case for d 1 = 2, that is,

\begin{matrix} leftalign-star \\ rightalign-odd y_{k} & align-even = ξ_{0, k} {y ˜}_{k} + MathClass-open(1 - ξ_{0, k} MathClass-close) \{MathClass-open(1 - ξ_{0, k - 1} MathClass-close) ξ_{1, k} {y ˜}_{k - 1} + MathClass-open[1 - MathClass-open(1 - ξ_{0, k - 1} MathClass-close) ξ_{1, k} MathClass-close] & rightalign-label & align-label \\ rightalign-odd & align-even \times MathClass-open(1 - ξ_{0, k - 2} MathClass-close) MathClass-open(1 - ξ_{0, k - 1} MathClass-close) ξ_{2, k} {y ˜}_{k - 2}\} & rightalign-label & align-label \end{matrix}

…”

Section: Problem Formulationmentioning

confidence: 99%

“…Models (2) and (3) can simultaneously describe consecutive packet dropouts and possible onestep, two-step, up to d 1 -step, and d 2 -step random transmission delays in networked systems. They are more general than those proposed in [3,13] where the multiple packet dropouts or random time…”

Section: Network Controlled System Modelingmentioning

confidence: 99%

“…The previous important issues have attracted considerable research attention in recent years, see, for example, [2][3][4][5] for the time delays, [6][7][8][9] for the packet dropouts, and [10][11][12] for both time delays and packet dropouts. The phenomena of time delays and/or packet dropouts happen stochastically in nature.…”

Section: Introductionmentioning

confidence: 99%

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Observer-based H _∞ control for networked systems with bounded random delays and consecutive packet dropouts

Sun

2013

Int. J. Robust. Nonlinear Control

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SUMMARYThis paper is concerned with the H ∞ control problem for a class of systems with bounded random delays and consecutive packet dropouts that exist in both sensor‐to‐controller channel and controller‐to‐actuator channel during data transmission. A new model is developed to describe possible random delays and packet dropouts by two groups of Bernoulli distributed stochastic variables. To avoid the state augmentation, a full‐order observer‐based feedback controller is designed via LMI approach. Based on the Lyapunov theory, a sufficient condition is provided to guarantee the closed‐loop networked system to be asymptotically mean‐square stable and achieve the prescribed H ∞ disturbance‐rejection‐attenuation level. The simulation examples illustrate the effectiveness of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

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\begin{matrix} leftalign-star \\ rightalign-odd y_{k} & align-even = ξ_{0, k} {y ˜}_{k} + MathClass-open(1 - ξ_{0, k} MathClass-close) \{MathClass-open(1 - ξ_{0, k - 1} MathClass-close) ξ_{1, k} {y ˜}_{k - 1} + MathClass-open[1 - MathClass-open(1 - ξ_{0, k - 1} MathClass-close) ξ_{1, k} MathClass-close] & rightalign-label & align-label \\ rightalign-odd & align-even \times MathClass-open(1 - ξ_{0, k - 2} MathClass-close) MathClass-open(1 - ξ_{0, k - 1} MathClass-close) ξ_{2, k} {y ˜}_{k - 2}\} & rightalign-label & align-label \end{matrix}

…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Network Controlled System Modelingmentioning

confidence: 99%

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Observer-based H _∞ control for networked systems with bounded random delays and consecutive packet dropouts

Sun

2013

Int. J. Robust. Nonlinear Control

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“…In these situations, conventional algorithms are not applicable and it is necessary to develop new ones that take these uncertainties into account. For example, estimation algorithms have been derived in Sun et al (2008), García-Ligero et al (2011) and Guo (2017), for systems with packet dropouts; in Sun and Xiao (2013), Caballero-Águila et al (2013) and Sun and Tian (2011), for random delays, and in Gao and Chen (2014), Hu et al (2012) and Pang and Sun (2015), considering missing measurements.…”

Section: Introductionmentioning

confidence: 99%

Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains

García-Ligero

Hermoso-Carazo

Linares-Pérez

2020

International Journal of Systems Science

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This paper addresses the linear least-squares estimation of a signal from measurements subject to stochastic sensor gain degradation and random delays during the transmission. These uncertainty phenomena, common in network systems, have traditionally been described by independent Bernoulli random variables. We propose a model that is more general and therefore has greater applicability to real-life situations. The model has two particular characteristics: firstly, the sensor gain degradation is represented by a white sequence of random variables with values in [0,1]; in addition, the absence or presence of delays in the transmission is described by a homogeneous three-state Markov chain, which reflects a possible correlation of delays at different sampling times. Furthermore, assuming that the measurement noise is one-step correlated, we obtain recursive prediction, filtering and fixed-point smoothing algorithms using the first and second-order moments of the signal and the processes present in the observation model. Simulation results for a scalar signal are provided to illustrate the feasibility of the proposed algorithms, using the estimation error variances as a measure of the quality of the estimators.

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“…The phenomena of packet dropouts and delays occur in a random way. One of the popular approaches to modelling the NCSs is the Bernoulli distribution model, see [1][2][3] for packet dropouts and [4][5][6] for transmission delays. Different from seperately considered the random delay and packet dropout problems, Sun presents a new model to conduct the above issues in a unified framework in [7] and the optimal linear filters in the linear minimum variance sense is derived.…”

Section: Introductionmentioning

confidence: 99%

H<sub>∞</sub> Filtering for Network-Based Systems with Consecutive Packet Dropouts and Delayed Measurements

Wang

Mao

et al. 2013

AMM

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The filtering problem is studied for a class of discrete-time systems under the effect of the networks. The investigated issues involved by the network include the consecutive packet dropouts and the one-step transmission delays which can be described by a sequence of mutually independent Bernoulli distributed stochastic variables. A full-order filter is designed such that the filtering error system is exponentially stable in the mean square and the performance is also achieved. Sufficient conditions are developed for the addressed problem and the desired filter is derived by means of the feasibility of certain linear matrix inequality. Numerical example is given to illustrate the effectiveness of the proposed method.

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Optimal linear estimators for systems with random measurement delays

Cited by 6 publications

References 19 publications

Observer-based H _∞ control for networked systems with bounded random delays and consecutive packet dropouts

Observer-based H _∞ control for networked systems with bounded random delays and consecutive packet dropouts

Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains

H<sub>∞</sub> Filtering for Network-Based Systems with Consecutive Packet Dropouts and Delayed Measurements

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Optimal linear estimators for systems with random measurement delays

Cited by 6 publications

References 19 publications

Observer-based H ∞ control for networked systems with bounded random delays and consecutive packet dropouts

Observer-based H ∞ control for networked systems with bounded random delays and consecutive packet dropouts

Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains

H<sub>∞</sub> Filtering for Network-Based Systems with Consecutive Packet Dropouts and Delayed Measurements

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Observer-based H _∞ control for networked systems with bounded random delays and consecutive packet dropouts

Observer-based H _∞ control for networked systems with bounded random delays and consecutive packet dropouts