Quantized Stabilization for T–S Fuzzy Systems With Hybrid-Triggered Mechanism and Stochastic Cyber-Attacks

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INTRODUCTIONNetworked control systems (NCSs) are described as the spatially distributed systems in which the sensors, actuators, and controllers are connected via a shared communication network. In comparison with traditional systems, the NCSs possess many advantages such as great flexibility, convenient maintenance, low cost of installation, and so on. Yet, along with these advantages, some challenging issues (bandwidth allocation, communication delay, packet dropouts, packet disorders, and channel fading) emerged and gave rise to much attention. Then most recently, the research on the NCSs has become a heated topic and many elegant results have been proposed. 1-29 For instance, in Reference 1, an optimal linear filter for the NCSs was designed with involving time-correlated fading channels.As for distributed NCSs, in Reference 2, both communication constraints (packet dropouts and quantization) and switching topology were addressed. Meanwhile, since communication delay inevitably exists and leads to poor performance, then References 3-7 deeply studied its negative effects when designing the controllers. In Reference 3,

Section: Codesigns On Observer and Controllermentioning

confidence: 99%

Event‐triggered observer‐based robust H_∞ control for networked control systems with unknown disturbance

Wang

Zhai

et al. 2020

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“…For example, in some literature, a typical ETS is given as

t_{k + 1} = \min false{t \in double-struckR false| false(false‖ x false(t_{k} false) - x false(t false) false‖ false) \geq σ false(false‖ x false(t false) false‖ false) false}

, where x ( t ) is the current state, x ( t k ) is the last released packet, t k is the most recent released instant, t k +1 is the next released instant, and σ is a constant threshold parameter. In the past two decades, different types of triggering conditions have been explored, which can be roughly classified into several categories, such as the absolute ETS, mixed ETS, hybrid ETS, static ETS, dynamic ETS, periodic/discrete ETS, adaptive ETS, and distributed ETS . It is worth noting that, in order to avoid ZENO phenomenon, Heemels et al and Yeu et al made a trade‐off between the time‐triggered control and event‐triggered control and proposed a periodic/discrete event‐triggered control for linear systems.…”

Section: Introductionmentioning

confidence: 99%

Resilient control of networked control systems under deception attacks: A memory‐event‐triggered communication scheme

Kunyu

Tian

Liu

et al. 2019

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179

104

Summary This paper considers resilient event‐triggered control problem for a class of networked systems subject to randomly occurring deception attacks. First, a new memory event‐triggered scheme (METS) is proposed to reduce the utilization of communication resources while maintaining desired system performance. Different from some existing event‐triggered schemes, some recently released packets are firstly utilized in the proposed METS, which provides a better flexibility to improve the system dynamics. Second, considering the security problem of the networked control systems, a randomly occurring deception attack model is employed where the bounded malicious signals are injected by the adversary. Considering both the effects of METS and deception attack, new type of networked control system model is constructed and the corresponding memory state‐feedback controller is designed. Then, sufficient conditions for the asymptotical stability of the systems are derived by using a Lyapunov functional technique. Finally, the obtained results are verified through a pendulum system, which demonstrates the effectiveness of the proposed methods.

“…Since the past several decades, fuzzy logic theory has been a popular methodology in deal with robust nonlinear control because the fuzzy model is capable of being an universal approximator of uncertain nonlinear systems 1‐4 . It's notable that the Takagi‐Sugeno (T‐S) fuzzy model 5 is one of the most commonly used fuzzy models, for example, nonlinear control synthesis via state/output‐feedback control 6‐8 . Meanwhile, T‐S model‐based fuzzy estimation has also been addressed on the topic of nonlinear filtering, 9‐11 nonlinear fault estimation (FE), 12,13 and so on.…”

Section: Introductionmentioning

confidence: 99%

Robust fault estimation of discrete‐time nonlinear plants via a comprehensive partition‐based switching scheme

Xie

Zang

Yue

et al. 2020

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The problem of robust fault estimation (FE) for a class of nonlinear systems in discrete-time domain is studied via proposing a comprehensive partition-based switching scheme. Both enhancement factors and attenuation factors are designed synchronously for accomplishing a comprehensive partition of the entire space spanned by the considered normalized fuzzy weighting functions. Thanks to the proposed comprehensive partition-based switching scheme, six subspaces would be obtained by identifying the real-time permutation order of the underlying parameters. Therefore, a novel fuzzy FE observer with six kinds of switching modes can be on-line implemented. As a result, the robust H ∞ performance index can be enhanced since much more information is employed than those reported results. Finally, simulation comparisons over the existing results are provided to show the obtained advantages of the article.