Event-triggered fault detection for networked control systems subject to packet dropout

Atitallah, Manel; Davoodi, Mohammadreza; Meskin, Nader

doi:10.1109/codit.2017.8102739

Cited by 8 publications

(22 citation statements)

References 21 publications

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“…For instance, in , the ETM invokes the events when the norm of the current state is larger than a constant. In , the ETM invokes the events when the norm of the error, between the current and last transmitted signals, is larger than a constant or monotonically decreasing time‐varying threshold.…”

Section: Introductionmentioning

confidence: 99%

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Liu

2018

Asian Journal of Control

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This paper is concerned with modeling and controlling under the mixed event‐triggered mechanism (ETM) for networked control systems (NCSs) with time‐varying delays and uncertainties. Firstly, an event‐triggered threshold is set by using both state and state‐independent informations in the mixed ETM. Then the event‐triggered NCSs with network‐induced time‐varying delays which exist in both sensor‐to‐controller and controller‐to‐actuator channels are modeled as a general time‐delay system. Based on the piecewise differentiable characteristic of the time‐varying delay and by using the approach of free weighting matrix and reciprocally convex, a less conservative criterion to be globally uniformly ultimately bounded (GUUB) stability and a controller design method are derived. Furthermore, an algorithm is proposed to obtain the desired mixed ETM and state‐feedback controller which can render the network load and control performance to reach an expected level. Compared with the relative and absolute ETMs, the proposed mechanism can effectively improve the transmission efficiency during the whole working time. Finally, a numerical example is given to show the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Liu

2018

Asian Journal of Control

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“…H∞ filtering and LMI Fading measurements [11,23] Quantization effects [74] Stochastic additive faults [15,28] Medium access constraints [14] Unknown transition probability [75] Comprehensive incomplete measurements [12,13,25,26] EM algorithm under the Bayesian framework Asynchronous measurements in distributed systems [54] Residual generating based on fault diagnosis filters and observers Additive faults & incomplete measurements [7,8,17,24,27,31,32,46,47] Attacks on sensors [73] Soft faults & packet dropouts [48] Actuator faults [42,45] Faulty periodic communication [30] Cyber attacks [39,76] Sliding mode observer Attacks on sensors [72] Unknown input observer False data injection attacks [35] Minimum-variance filtering and Kalman filtering Cyber attacks [20,37,64,70] Additive faults [49] Homomorphic encryption [77] Particle filtering Cyber attacks [41,69] Strong tracking filtering Packet dropouts [78] Distributed resilient filtering Sensor degradation [79] Self-learning approaches Additive sensor fault [71,75,…”

Section: Methodologies Major Problems Addressed Literaturementioning

confidence: 99%

“…Event‐triggered schemes were integrated into the measurement model in [27] of polynomial‐fuzzy‐model systems, and an H ∞ based fault detection filter was designed against additive faults. In [28], packet dropouts in networked systems with event‐triggered mechanisms were modeled using Markov chains. A robust residual generator was designed using the dynamic parity space fault detection method with a specific optimization index.…”

Section: Recent Advancesmentioning

confidence: 99%

Model‐based fault diagnosis of networked systems: A survey

Song

2021

Asian Journal of Control

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This paper reviews the latest development of fault diagnosis techniques of networked systems. Considering various challenges raised by unreliable local sensors, signal digitization, data transmission, as well as distributed architecture, recent progress on model‐based fault diagnosis of networked systems are overviewed. We summarize the typical application examples and the typical treatment methods related to scientific problems for better understanding the framework of this area. Some promising future fault diagnosis techniques in various scenarios, such as distributed systems, with communication protocols, with cyber security issues, with intelligent algorithm challenges, as well as within active fault diagnosis framework are discussed.

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“…Over the last decades, industrial processes have become more complex and the demands for precise, reliable, and secure procedures followed the same path. In order to fulfill those demands, different approaches have been proposed in the literature and, in particular, one of them is the socalled Fault Detection and Isolation (FDI) approach, which primarily detects faults and rearranges the system to minimize the possible losses and/or chances of accidents, see for example (Wang et al, 2019), (Zhou et al, 2017), and (Atitallah et al, 2018). The FDI framework is widely applied to different fields in engineering as presented in the review (Venkatasubramanian et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Fault Detection H2 Filter For Markov Jump Linear Systems

Carvalho

Oliveira

Valle

2020

Anais Do Congresso Brasileiro De Automática 2020

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This work focuses on the Fault Detection (FD) problem in the Markovian Jump Linear System framework for the discrete-time domain, under the assumption that the Markov chain mode is not directly accessible. This assumption poses new challenges, since the filter responsible for the residue generation no longer depends on the Markov chain mode. For modeling this type of situation, a Hidden Markov chain (O(k), Ô(k)) is considered, with O(k) corresponding to the hidden part and Ô(k), to the observable part. The main result is the design of an H2 Fault Detection Filter (FDF) that depends only on the estimated mode Ô(k), obtained through a formulation based on Linear Matrix Inequalities (LMIs). In order to illustrate the usability of the proposed approach, we consider as an illustrative example a plant with coupled tanks subject to two distinct faults.

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Event-triggered fault detection for networked control systems subject to packet dropout

Cited by 8 publications

References 21 publications

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

Model‐based fault diagnosis of networked systems: A survey

Asynchronous Fault Detection H2 Filter For Markov Jump Linear Systems

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