Resilient State Estimation for Complex Dynamic Networks With System Model Perturbation

Duan, Peihu; Lv, Guannan; Duan, Zhisheng; Lv, Yuezu

doi:10.1109/tcns.2020.3035759

Cited by 25 publications

(9 citation statements)

References 35 publications

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“…The state trajectory of x (2) 1 (s) and its estimates problem for DCNs has gained ongoing research interest during the past several decades. 55,56 For the centralized state estimation problem, the impact of the network topology on the system dynamics is reflected through the coupling configuration matrix and the inner-coupling matrix by using the augmentation approach. One of our future research topics is the study of distributed MHE for nonlinear DCNs with the event-triggered scheme.…”

Section: F I G U R Ementioning

confidence: 99%

Centralized moving‐horizon estimation for a class of nonlinear dynamical complex networks under event‐triggered transmission scheme

Gao

Wang

Sheng

et al. 2022

Intl J Robust & Nonlinear

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This article is concerned with the problem of event-triggered centralized moving-horizon state estimation for a class of nonlinear dynamical complex networks. An event-triggered scheme is employed to reduce unnecessary data transmissions between sensors and estimators, where the signal is transmitted only when certain condition is violated. By treating sector-bounded nonlinearities as certain sector-bounded uncertainties, the addressed centralized moving-horizon estimation problem is transformed into a regularized robust least-squares problem that can be effectively solved via existing convex optimization algorithms. Moreover, a sufficient condition is derived to guarantee the exponentially ultimate boundedness of the estimation error, and an upper bound of the estimation error is also presented. Finally, a numerical example is provided to demonstrate the feasibility and efficiency of the proposed estimator design method.

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Section: F I G U R Ementioning

confidence: 99%

Centralized moving‐horizon estimation for a class of nonlinear dynamical complex networks under event‐triggered transmission scheme

Gao

Wang

Sheng

et al. 2022

Intl J Robust & Nonlinear

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“…with the known p and q, one has p(𝛾 k+1 = i|Y i,k , Γi,k ) = p(𝛾 k+1 |γ i,k ). Substituting ( 30), (31), or (32) into (29) and noting (28), we obtain the value of γi,k+1 . Finally, we need to amend the established theorems with the estimated γi,k+1 .…”

Section: Distributed Resilient Recursive Bayesian Filteringmentioning

confidence: 99%

“…For instance, a robust nonfragile Kalman filter has been designed in Yang and Wang [30] for a class of linear systems with norm-bounded uncertainties. Furthermore, the problem of resilience estimation has been studied in Duan et al [31] for a class of complex networks with perturbations.…”

Section: Introductionmentioning

confidence: 99%

Distributed resilient filtering for a class of nonlinear dynamical systems subject to hybrid cyber attacks

Liu

Ding

2021

Asian Journal of Control

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In this paper, the distributed resilient filtering issue is studied for a class of nonlinear dynamical systems subject to hybrid cyber attacks and gain disturbances.The gain perturbation could come from data rounding errors due to the fixed word length and is modeled by multiplicative noises. The addressed hybrid cyber attacks consist of both denial-of-service (DoS) attacks and deception attacks, in which the injected signal obeys a Gaussian mixture distribution. With the help of Kalman filtering theory, an optimally distributed filtering scheme is developed and the desired filter gain is derived by minimizing the filtering error covariance at each time instant. Furthermore, when the statistical information on DoS attacks is not available, the states of DoS attacks are identified in real-time by resorting to a maximum posterior probability estimation approach combined with the classical Bayesian rule. Based on the developed formulas and the identified state, a suboptimal distributed filtering algorithm is established to realize the state estimation. Finally, a simulation example shows the effectiveness of the proposed distributed resilient filtering algorithm.

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“…To guarantee satisfactory performances of CPSs, one critical issue is ensuring a reliable transmission of tremendous sensing data from wireless sensors to remote estimators for state estimation [4,14]. As communication resources are always limited, it is important to synthesize a scheduler for sensors to achieve a better trade-off between the state estimation performance and the communication overhead.…”

Section: Introductionmentioning

confidence: 99%

Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework

Duan¹,

He²,

Huang³

et al. 2022

Preprint

Self Cite

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This paper investigates sensor scheduling for state estimation of complex networks over shared transmission channels. For a complex network of dynamical systems, referred to as nodes, a sensor network is adopted to measure and estimate the system states in a distributed way, where a sensor is used to measure a node. The estimates are transmitted from sensors to the associated nodes, in the presence of one-step time delay and subject to packet loss. Due to limited transmission capability, only a portion of sensors are allowed to send information at each time step. The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors. Under a distributed state estimation framework, this problem is reformulated as a Markov decision process, where the one-stage reward for each node is strongly coupled. The feasibility of the problem reformulation is ensured. In addition, an easy-to-check condition is established to guarantee the existence of an optimal deterministic and stationary policy. Moreover, it is found that the optimal policies have a threshold, which can be used to reduce the computational complexity in obtaining these policies. Finally, the effectiveness of the theoretical results is illustrated by several simulation examples.

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Resilient State Estimation for Complex Dynamic Networks With System Model Perturbation

Cited by 25 publications

References 35 publications

Centralized moving‐horizon estimation for a class of nonlinear dynamical complex networks under event‐triggered transmission scheme

Centralized moving‐horizon estimation for a class of nonlinear dynamical complex networks under event‐triggered transmission scheme

Distributed resilient filtering for a class of nonlinear dynamical systems subject to hybrid cyber attacks

Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework

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