Resilient adaptive control of switched nonlinear cyber-physical systems under uncertain deception attacks

Li, Zhanjie; Zhao, Jun

doi:10.1016/j.ins.2020.07.022

Cited by 103 publications

(98 citation statements)

References 41 publications

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“…Remark 8: Some similar studies have been carried out for a class of cyber-physics systems in the existing literature [28], [37]. In [28] and [37], the weight β(t) is required to be strictly positive in this article. This implies that the stability of the system may not be guaranteed when the sign of β(t) changes.…”

Section: B Application To Chemical Reactor Systemmentioning

confidence: 99%

“…Obviously, the presented results provide a unified framework for addressing the adaptive resilient control problem of nonlinear time-delay systems with strict-feedback form. 2) Different from [28] and [37], the conservative assumption that the sign of attack weight is strictly required to be positive can be fully removed based on the Nussbaum gain technique. Besides, the bounds of the actuation effectiveness and stuck faults are not required in this article.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, the bounds of the actuation effectiveness and stuck faults are not required in this article. Moreover, by integrating with the commandfiltered backstepping technique and fractional calculus, a modified fractional-order command filter (FOCF) is developed such that the computational burden of the existing methods in [26] and [27] is greatly relaxed and filter performance is obviously improved in comparison to [28] and [37].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

Song

Park

Zhang

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

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This article considers neural network (NN)-based adaptive finite-time resilient control problem for a class of nonlinear time-delay systems with unknown fault data injection attacks and actuator faults. In the procedure of recursive design, a coordinate transformation and a modified fractionalorder command-filtered (FOCF) backstepping technique are incorporated to handle the unknown false data injection attacks and overcome the issue of "explosion of complexity" caused by repeatedly taking derivatives for virtual control laws. The theoretical analysis proves that the developed resilient controller can guarantee the finite-time stability of the closed-loop system (CLS) and the stabilization errors converge to an adjustable neighborhood of zero. The foremost contributions of this work include: 1) by means of a modified FOCF technique, the adaptive resilient control problem of more general nonlinear time-delay systems with unknown cyberattacks and actuator faults is first considered; 2) different from most of the existing results, the commonly used assumptions on the sign of attack weight and prior knowledge of actuator faults are fully removed in this article. Finally, two simulation examples are given to demonstrate the effectiveness of the developed control scheme.

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Section: B Application To Chemical Reactor Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

Song

Park

Zhang

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

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“…In the context of industry 4.0, industrial control systems (ICSs) are closely combined with industrial wireless sensor networks (IWSNs) to form industrial cyber-physical systems (ICPSs), where the physical space and the cyber space are deeply integrated, 1 such as power systems, smart grid systems, and transportation systems. [2][3][4] ICPSs have become a core technology to support and lead a new round of industrial transformation, due to their various advantages, such as lower configuration and maintenance cost, flexibility, reliability, and safety.…”

Section: Introductionmentioning

confidence: 99%

Memory dynamic output feedback controller based on asynchronous event‐triggered robust H‐infinity control for nonlinear ICPSs under data injection attacks

Qin

Sun

2021

Intl J Robust & Nonlinear

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Aiming at reducing the influence caused by data injection attacks, an asynchronous event-triggered robust H-infinity control (AERHC) is proposed for the stability of nonlinear ICPSs suffering from data injection attacks. A robust H-infinity memory dynamic feedback control technique is designed to guarantee asymptotic stability of the closed-loop system under a Lyapunov functional and the robust H ∞ stability criterion. Furthermore, a memory dynamic feedback controller which has five controller gains different from each other for mitigating the conservative of nonlinear ICPSs and for resisting data injection attacks in ICPSs. Meanwhile, an asynchronous event-triggered strategy is adopted to reduce the data communication frequency among nonlinear ICPSs components, and it effectively handle the influence of coupling problem between the measurement channel and the control channel. Finally, simulation tests are conducted by using MATLAB to control the robustness of the motor angle position of electromechanical system by using AERHC, which verify that the correctness and effectiveness of the proposed method.

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“…Cyber attacks are classified into three types: repeated attack, denial of service attack, and deception attack, in which the biggest threat to network security is deception attacks. Some interesting results associated with deception attacks have been given [31][32][33][34]. For instance, a secure filter is devised for the delayed stochastic nonlinear systems with a novel multiple-channel attack model [33].…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered $H_{\infty}$ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

et al. 2020

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This paper concentrates on the event-triggered H ∞ filter design for the discrete-time Markovian jump neural networks under random missing measurements and cyber attacks. Considering that the controlled system and the filtering can exchange information over a shared communication network which is vulnerable to the cyber attacks and has limited bandwidth, the event-triggered mechanism is proposed to relieve the communication burden of data transmission. A variable conforming to Bernoulli distribution is exploited to describe the stochastic phenomenon since the missing measurements occur with random probability. Furthermore, seeing that the communication networks are vulnerable to external malicious attacks, the transferred information via the shared communication network may be changed by the injected false information from the attackers. Based on the above consideration, sufficient conditions for the filtering error system to maintain asymptotically stable are provided with predefined H ∞ performance. In the end, three numerical examples are given to verify the proposed theoretical results.

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Resilient adaptive control of switched nonlinear cyber-physical systems under uncertain deception attacks

Cited by 103 publications

References 41 publications

Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

Memory dynamic output feedback controller based on asynchronous event‐triggered robust H‐infinity control for nonlinear ICPSs under data injection attacks

Event-Triggered $H_{\infty}$ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

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Resilient adaptive control of switched nonlinear cyber-physical systems under uncertain deception attacks

Cited by 103 publications

References 41 publications

Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

Memory dynamic output feedback controller based on asynchronous event‐triggered robust H‐infinity control for nonlinear ICPSs under data injection attacks

Event-Triggered H ∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

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Event-Triggered $H_{\infty}$ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks