Stability analysis on networked control systems under double attacks with predictive control

Yang, Hongjiu; Xu, Hao; Xia, Yuanqing; Zhang, Jinhui

doi:10.1002/rnc.4840

Cited by 18 publications

(16 citation statements)

References 32 publications

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“…In this regards, a few attention has paid for filtering systems. [33][34][35][36][37] In this article, we examine the problem of T-S filtering systems in a network. Compared with the existing studies, 38,39 we develop new stochastic stability with resulting error for T-S fuzzy systems with Brownian motion and time-varying dependent delay under the unknown attacks.…”

Section: Introductionmentioning

confidence: 99%

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Retracted: State estimation of Takagi‐Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

Shamrooz

2021

Adaptive Control & Signal

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This article concerns the event‐triggered fuzzy filter design for Takagi‐Sugeno (T‐S) fuzzy systems subject to deception attacks under the stochastic multiple time‐varying delays. A sequence of random variables, which are mutually independent but obey the Bernoulli distribution, is introduced to account for the randomly occurring communication delays. In order to efficiently utilize limited network communication bandwidth resources, the event‐triggering scheme is adopted. A fuzzy filter with the attacked input signal is presented. Moreover, due to communication delays caused by event‐triggering schemes and transmission, the filter adopts non‐synchronous premise variables with the system. Then, by utilizing a model transformation technique, the fuzzy systems are developed. Furthermore, using the piecewise Lyapunov functional method technique, the resulting criterion provides sufficient conditions to ensure that fuzzy systems under deception attacks are stochastically stable with an H∞ performance. Accordingly, the conditions for the co‐design of the fuzzy filter and event‐triggering schemes are given. Finally, numerical simulation with the industrial process provided to verify the proposed event‐triggered design.

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Section: Introductionmentioning

confidence: 99%

“…Furthermore, due to sudden or unexpected changes in the system, system faults may occur, degrade the system's normal performance, and even lead to equipment breakdown. In this regards, a few attention has paid for filtering systems 33‐37 …”

Section: Introductionmentioning

confidence: 99%

Retracted: State estimation of Takagi‐Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

Shamrooz

2021

Adaptive Control & Signal

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“…In the new BIT‐NAZA robot system, various uncertain disturbances cannot be ignored. Due to neural networks (see Figure 5) have the ability to approximate arbitrary nonlinear functions, 39,40 a continuous smooth function

S

is defined. Then we use the function to estimate various uncertain disturbances of robot.…”

Section: Controller Designmentioning

confidence: 99%

Neural networks‐based sliding mode tracking control for the four wheel‐legged robot under uncertain interaction

Wang

2021

Intl J Robust & Nonlinear

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When considering the accuracy of tracking control, physical interaction such as structural uncertainties and external dynamics is the main challenge in actual engineering scenarios, especially for the complex robot system. In this article, a neural network‐based sliding mode tracking control scheme (SMCR) is presented for the developed four wheel‐legged robot (BIT‐NAZA) under the uncertain interaction. First, a non‐singular fast terminal function based on the kinematic model is proposed for path tracking, which improves the motion quality during the approach movement and the sliding mode movement. At the same time, it can reduce the influence of uncertain disturbances on the premise of ensuring the path tracking control accuracy via neural networks. Finally, some demonstrations using the autonomous platform of the BIT‐NAZA robot are employed to evaluate the robustness and effectiveness of the hybrid algorithm.

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“…At the same time, the controller should achieve remote monitoring and adjustment of plants over a communication network. 9 In recent years, many control methods have been proposed to solve the problem caused by the time-delay in the NCS. In the early studies, many scholars think that the network time-delay is a bounded sequence.…”

Section: Introductionmentioning

confidence: 99%

Predictive control compensation for networked control system with time-delay

Tian

Wang

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This study proposes a novel predictive control compensation method to deal with random time-delay in networked control system. Different from other compensation strategies, this study adopts different compensation strategies for input time-delay and output time-delay to improve the control effect. First, for the input channel time-delay, the corresponding buffer is set by the time-stamp in the packet. Combining the historical output value and the control variable, the fast implicit generalized predictive control algorithm is adopted to design the predictive controller to compensate for the input channel time-delay. Second, for the output channel time-delay, the controller cannot measure it. A control compensator is designed by adding a feedback loop. The output control variable of the predictive controller is adjusted by predicting the error between the actual control signal and the controller output at the historical sampling time, so as to compensate for the time-delay of the output channel. In addition, the stability of the proposed time-delay compensation method is analyzed. Finally, simulation and experimental results show that the proposed method has good control and compensation effect, and improves the output performance of the system. The networked control system with random time-delay is stable. Furthermore, the simulation results also show that this time-delay compensation method needs less computing time and is more suitable for the practical applications.

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Stability analysis on networked control systems under double attacks with predictive control

Cited by 18 publications

References 32 publications

Retracted: State estimation of Takagi‐Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

Retracted: State estimation of Takagi‐Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

Neural networks‐based sliding mode tracking control for the four wheel‐legged robot under uncertain interaction

Predictive control compensation for networked control system with time-delay

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