Distributed networked set-membership filtering with ellipsoidal state estimations

Xia, Nan; Yang, Fang; Han, Qing‐Long

doi:10.1016/j.ins.2017.12.010

Cited by 60 publications

(15 citation statements)

References 45 publications

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“…2) the set-membership state estimation problem for linear timevarying systems subject to IMOs [39], [42]; 3) state estimation for time-delayed systems with gain variations subject to IMOs [12]; 4) state estimation for neural networks subject to IMOs [27]- [29]; and 5) the improvement of the state estimation performance by using some latest optimization algorithms [25], [26].…”

Section: An Illustrative Examplementioning

confidence: 99%

Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case

Zou

Wang

Dong

et al. 2022

IEEE Trans. Cybern.

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This paper is concerned with the energy-to-peak state estimation problem for a class of linear discrete-time systems with energy-bounded noises and intermittent measurement outliers (IMOs). In order to capture the intermittent nature, two sequences of step functions are introduced to model the occurrence of the IMOs. Furthermore, two special indices (i.e. minimum and maximum interval lengths) are adopted to describe the "occurrence frequency" of IMOs. Different from the considered energy-bounded noises, the outliers are assumed to have their magnitudes larger than certain thresholds. In order to achieve a satisfactory performance constraint on the energy-to-peak state estimation under the addressed kind of measurement outliers, a novel parameter-dependent (PD) state estimation strategy is developed to guarantee that the measurements contaminated by outliers would be removed in the estimation process. The proposed PD state estimation method is essentially a two-step process, where the first step is to examine the appearing and disappearing moments for each IMO by using a dedicatedly constructed outlier detection scheme, and the second step is to implement the state estimation task according to the outlier detection results. Sufficient conditions are obtained to ensure the existence of the desired estimator, and the gain matrix of the desired estimator is then derived by solving a constrained optimization problem. Finally, a simulation example is presented to illustrate the effectiveness of our developed PD state estimation strategy.

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Section: An Illustrative Examplementioning

confidence: 99%

Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case

Zou

Wang

Dong

et al. 2022

IEEE Trans. Cybern.

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“…Since all the system constraints are transformed into LMIs, the optimisation problems can be solved by the semi‐definite programming via an interior‐point algorithm [34]. The interior‐point algorithm has a polynomial‐time complexity

O false(ℓ {scriptM}^{3} false)

, where

ℓ

is the total row size of the main LMIs and

M

is the total number of scalar decision variables of the main LMIs [25, 26]. In our case, for the state estimation LMI constraint (8), we have

ℓ_{1} = 2 n_{x} + r + p + 1

and

{scriptM}_{1} = false(n_{x}^{2} / 2 false) + false(\frac{1}{2} + n_{y} false) n_{x} + 3

.…”

Section: Model Predictive Control With Set‐membership State Estimationmentioning

confidence: 99%

Output feedback model predictive control based on set‐membership state estimation

Qiu

Yang

Zhu

et al. 2020

IET Control Theory & Applications

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“…Compared with the traditional point estimation method, the SMF does not have high requirements for the description of the disturbances and for sensor precision. Due to these advantages, the SMF has a wide range of applications in state estimation and trajectory tracking (see, e.g., Ge et al, 2019;Xia et al, 2018;Zhang et al, 2019Zhang et al, , 2020cMeslem and Hably, 2020;Zhou et al, 2013). However, to the best of the authors' knowledge, SMF is rarely used to solve the problem of AGV trajectory estimation and tracking.…”

Section: Introductionmentioning

confidence: 99%

Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises

Yang

Zhang

et al. 2021

Transactions of the Institute of Measurement and Control

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This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.

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Distributed networked set-membership filtering with ellipsoidal state estimations

Cited by 60 publications

References 45 publications

Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case

Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case

Output feedback model predictive control based on set‐membership state estimation

Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises

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