Modified particle filter and Gaussian filter with packet dropouts

Yang, Chao; Fang, Huajing

doi:10.1002/rnc.4060

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…By Schur complement, inequality (12) can guarantee H < 0, then E{ΔV(k)} < 0 can be established. Overall, it implies that system (11) under (k) = 0 is mean square asymptotically stable.…”

Section: Proof Consider the Following Lyapunov Functionmentioning

confidence: 99%

“…system (11) possesses a given  ∞ system performance level . In addition, the DOFC matrices in (8) can be derived as…”

Section: Proof Consider the Following Lyapunov Functionmentioning

confidence: 99%

“…Some studies have been recently considered data dropout problems. In particular, stability condition was discussed in Reference 10, filter problem was addressed in Reference 11, state estimation problem was investigated in Reference 12, and dynamic feedback control problem was proposed in Reference 9. Several other contributions make it clear that induced delay and data packet dropouts, as two major issues in NCSs, may lead to limitations in the system performance and even cause fault operation.…”

Section: Introductionmentioning

confidence: 99%

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Event‐triggered dynamic output feedback control for networked nonlinear systems

Liu

Shi

2020

Intl J Robust & Nonlinear

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In this article, the problem of event-triggered dynamic output feedback control is investigated for a class of networked nonlinear systems with mixed delays and packet dropouts. By applying the cone complementary linearization technique, sufficient conditions are developed such that the resulting closed-loop system dynamics is mean square asymptotically stable with a desired  ∞ performance level. The effectiveness of the proposed method is demonstrated via an illustrative example.

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“…By Schur complement, inequality (12) can guarantee H < 0, then E{ΔV(k)} < 0 can be established. Overall, it implies that system (11) under (k) = 0 is mean square asymptotically stable.…”

Section: Proof Consider the Following Lyapunov Functionmentioning

confidence: 99%

“…system (11) possesses a given  ∞ system performance level . In addition, the DOFC matrices in (8) can be derived as…”

Section: Proof Consider the Following Lyapunov Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Event‐triggered dynamic output feedback control for networked nonlinear systems

Liu

Shi

2020

Intl J Robust & Nonlinear

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“…2,3 However, due to cyber-attacks 4 and various network-induced constraints, for example, channel congestion, delay, signal degradation, and so forth, packet losses may occur during network transmission. 5,6 In the past two decades, a large number of filtering and state estimation techniques-such as H ∞ filtering, 7 particle filtering, 8 non-fragile state estimation, 9 and robust distributed estimation 10,11 -have been developed for systems with packet losses. According to the observability of packet losses, systems can be divided into two classes: In Figure 1A, a system, in which the estimator cannot observe the packet-loss status, F I G U R E 1 UPL and OPL systems is called a system with unobservable packet losses (UPL) or a UPL system; the one in Figure 1B, in which the estimator can observe the packet-loss status, is called a system with observable packet losses (OPL) or an OPL system.…”

Section: Research Background and Advancementmentioning

confidence: 99%

State estimation for systems with unobservable packet losses: Approximate estimation, stability, and performance analysis

Lin

Lam

Wang

et al. 2021

Intl J Robust & Nonlinear

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For a system with packet losses, if the estimator can observe the status of packet losses, it is called a system with observable packet losses (an OPL system); otherwise, it is called a system with unobservable packet losses (a UPL system). We obtain the optimal estimator (OE) for UPL systems, which consists of an exponentially increasing number of items, and thus is computationally intractable. To address the computation issue, we design an approximate optimal estimator (AOE), which can be computed recursively. The proposed AOE features a theoretically-proven stability condition and a theoretically-guaranteed superiority to the optimal linear estimator (OLE). Specifically, for stability, we prove that for a stable UPL system, both the OE and the proposed AOE are stable; for performance, we show that both the OE and the proposed AOE are superior to the OLE in the mean sense. Then, we obtain a tight upper bound of the performance deviation between the OE and the proposed AOE. Finally, numerical examples are presented to illustrate the obtained results and the effectiveness of the proposed AOE in estimating system states when the packet-loss status, that is, the private information of packet losses, cannot be observed.

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“…Recently, development of PFs have been considered in networked control systems [1], [6], [27], [28]. More specifically, the problem of cyber attack detection is briefly and generally considered using PF in [29] where the residual norm is simply compared with a deterministic threshold for a three tank system.…”

mentioning

confidence: 99%

Modified Particle Filters for Detection of False Data Injection Attacks and State Estimation in Networked Nonlinear Systems

Sadeghzadeh-Nokhodberiz

Meskin

Hasanzadeh

2022

IEEE Access

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Networked control systems which transfer data over communication networks may suffer from malicious cyber attacks by injecting false data to the transferred information. Such attacks can cause performance degradation of the closed-loop system and the filtering problem. The sequential importance sampling (SIS) particle filtering (PF) methods employ the sequential Monte Carlo approach to estimate the generally non-Gaussian posterior probability density function (pdf) for Bayesian estimation of generally non-linear non-Gaussain systems. In this paper, it is firstly shown that with the normal SIS PF, the injected false data to the networked systems remains stealthy and therefore it is not possible to reduce the degrading effect of the attack on the estimation. However, with a modification in the proposal pdf, a modified SIS PF is then proposed which guarantees the attack detectability where the attacked measurements are incorporated in the particle generation process and thus the particles are updated and make the attack detectable. Using the derived thresholds and under small enough measurement noises, it is also proved that no false alarm occurs. After estimation of the attack value, the posterior pdf conditioned on truly detected attack leads to an estimation equivalent to the attack free SIS PF in terms of estimation bias and estimation covariance error. Finally, the accuracy of the presented concepts is demonstrated for a networked interconnected four-tank system.INDEX TERMS Networked control systems (NCS), cyber-security, sequential Monte Carlo, particle filtering, convergence analysis. I. INTRODUCTIONRecently, many industrial systems such as smart grids rely on communication networks to send their information to a computational unit. The combination of physical systems, communication networks, and computational devices forms a cyber-physical system (CPS) [1]. There are many research works devoted to the problem of networked control systems (NCS), more recently, in different control theory areas, on top of them networked controller design and state estimation [2], [3]. However, in spite of advantageous of such systems such as remote computations, they may suffer from network constraints and security issues [4]. Limited computational andThe associate editor coordinating the review of this manuscript and approving it for publication was Hosam El-Ocla .

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Modified particle filter and Gaussian filter with packet dropouts

Cited by 10 publications

References 31 publications

Event‐triggered dynamic output feedback control for networked nonlinear systems

Event‐triggered dynamic output feedback control for networked nonlinear systems

State estimation for systems with unobservable packet losses: Approximate estimation, stability, and performance analysis

Modified Particle Filters for Detection of False Data Injection Attacks and State Estimation in Networked Nonlinear Systems

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