Variance-constrained filtering for discrete-time genetic regulatory networks with state delay and random measurement delay

Chen, Dongyan; Chen, Weilu; Hu, Jun; Liu, Hongjian

doi:10.1080/00207721.2018.1542045

Cited by 29 publications

(15 citation statements)

References 44 publications

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“…By combining (6) and (32), the full state estimation error can be expressed as e i k+1 =x i k+1 − x i k+1 = Q s,k+1 e i s,k+1 . The upper bound of the full state estimation error covariance Φ i k+1 can be computed as…”

Section: Full State Estimationmentioning

confidence: 99%

“…In recent years, the distributed state estimation problem over a sensor network has received increasing attention due to its wide application in such areas as navigation, tracking, and supervision systems. [1][2][3][4][5][6] One critical issue arising from distributed state estimation problems is to design distributed state estimators based on dynamic systems with state constraints, which are ubiquitous in various engineering applications, by only utilizing local neighbors' information rather than global information. This article will focus on the distributed state estimation problem for uncertain dynamic systems with state constraints.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Kalman filtering for uncertain dynamic systems with state constraints

Duan

2020

Intl J Robust & Nonlinear

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This article addresses the distributed state estimation problem for uncertain time-varying dynamic systems with state constraints over a sensor network. By using a null space method, the distributed state estimation problem for uncertain dynamic systems with state constraints can be cast into a new unconstrained distributed state estimation problem for reduced uncertain dynamic systems. A constrained distributed Kalman filter is proposed, and it is shown that the full state estimates can be recovered at any time and satisfy the constraints. An optimized upper bound of the estimation error covariance of each sensor is obtained, and the corresponding gains are designed. The application conditions of the proposed algorithm are mild, and they can be off-line checked. Furthermore, the computational requirements in this article are also reduced compared with the existing results. Finally, the performance of the proposed filter algorithm is demonstrated through numerical simulations.

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Section: Full State Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Kalman filtering for uncertain dynamic systems with state constraints

Duan

2020

Intl J Robust & Nonlinear

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“…It is well recognized that the state delays exist in many real-word systems. Thus, many results have been developed to tackle the delays and avoid the great deterioration of system performance (Chen et al, 2019;Chen & Xu, 2014). For example, in (Du et al, 2016), an optimal filtering algorithm has been developed for a class of networked stochastic systems, where both the state delay and missing measurements have been taken into account.…”

Section: Introductionmentioning

confidence: 99%

Distributed filtering for delayed nonlinear system with random sensor saturation: a dynamic event-triggered approach

2021

Systems Science & Control Engineering

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This paper is concerned with the distributed filtering problem for a class of delayed nonlinear systems with random sensor saturation (RSS) under a dynamic event-triggered mechanism. The nonlinear function is assumed to satisfy the Lipschitz condition. A dynamic event-triggered mechanism is employed to further reduce the innovation transmission frequencies among the adjacent nodes. Both the Bernoulli distributed random variables and saturation function are employed to model the phenomenon of RSS. The aim of this paper is to design a sub-optimal filter such that the covariance of the filtering error has an upper bound, which is minimized by appropriately computing the filter gain. Furthermore, the error boundedness is analysed and a sufficient criterion is presented to ensure that the filtering error is mean-square bounded. Finally, a numerical example is provided to verify the effectiveness of the proposed filtering algorithm.

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“…In recent years, the analysis methods of complex dynamical networks have played an important role in the big data transmissions, information processing and power grid (Baran & Rzysko, 2020;Bonello et al, 2020;Chen et al, 2019;Ding et al, 2019;Hernandez-torres et al, 2020;Wang et al, 2019). Recently, the networked control technology brings many conveniences to industrial development and technological advancements (Chen et al, 2020;Hu et al, 2017;Zou, Wang, Dong et al, 2020;.…”

Section: Introductionmentioning

confidence: 99%

State estimation via prediction-based scheme for linear time-varying uncertain networks with communication transmission delays and stochastic coupling

Jia

et al. 2021

Systems Science & Control Engineering

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Variance-constrained filtering for discrete-time genetic regulatory networks with state delay and random measurement delay

Cited by 29 publications

References 44 publications

Distributed Kalman filtering for uncertain dynamic systems with state constraints

Distributed Kalman filtering for uncertain dynamic systems with state constraints

Distributed filtering for delayed nonlinear system with random sensor saturation: a dynamic event-triggered approach

State estimation via prediction-based scheme for linear time-varying uncertain networks with communication transmission delays and stochastic coupling

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