Novel fuzzy event-triggered adaptive control for nonlinear systems with input hysteresis

Chen, Zicong; Wang, Jianhui; Ma, Kemao; Zhu, Peisen; He, Biaotao; Zhang, Chunliang

doi:10.1007/s00500-021-05656-x

Cited by 8 publications

(7 citation statements)

References 36 publications

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“…Different from those studies, the generalized N -order free-matrix-based inequality is handle the stabilization problem of T-S fuzzy systems in the present study, which has some inequalities as particular cases. For example, substituting N = 0 in (10), it can be reduced to Jensen's inequality which has been utilized for T-S fuzzy systems in [17].…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the various nonlinear dynamical systems such as Rossler's system [2], Chua's circuit [8], mass-spring system [9], have been formulated as T-S fuzzy systems to study the stabilization problem. To tackle the stabilization issue of T-S fuzzy systems, several control techniques have been designed in literature, for example, adaptive control [10], impulsive control [11], mixed H ∞ passive control [12], SDC [13], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy membership function-dependent switched control for nonlinear systems with memory sampled-data information

Visakamoorthi

Subramanian²,

Muthukumar³

2022

Soft Comput

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In this paper, a fuzzy memory-based coupling sampled-data control (SDC) is designed for nonlinear systems through the switched approach. Compared with the usual SDC scheme, by employing the Bernoulli sequence, a more general coupling switched SDC that involving the signal transmission delay is designed. The Lyapunov-Krasovskii Functional (LKF) is presented with the available characteristics of the membership function, and a coupling sampling pattern, for the T-S fuzzy systems.Based on LKF, together with time derivative information of membership function, and the generalized N -order free-matrix-based inequality, the suitable conditions are obtained in terms of linear matrix inequalities (LMIs) for guaranteeing the asymptotic stability and stabilization of the concerned system. Then the desired fuzzy coupling SDC gain is attained from the solvable LMIs. In the end, two examples are given to validate the derived theoretical results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy membership function-dependent switched control for nonlinear systems with memory sampled-data information

Visakamoorthi

Subramanian²,

Muthukumar³

2022

Soft Comput

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“…Authors [16] focused on the state estimation of switched memristive neural networks with nonhomogeneous sojourn probabilities by introducing a dynamic EB mechanism. Fuzzy event-triggered adaptive control in [17] was tackled for nonlinear systems with input hysteresis. Utilizing an improved EBS, it aimed to derive the robust H ∞ control gains design of uncertain networked systems with semi-Markovian jump parameters [18] and based on fault-tolerant control and EBS, authors [19] addressed synchronization of TCS fuzzy Markovian time-delay neural networks.…”

Section: Introductionmentioning

confidence: 99%

Event-based finite-time quantized synchronization for discrete Markovian neural networks

Liu

Shi

et al. 2022

Preprint

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This paper is investigated with the robust finite-time synchronization of discrete-time Markovian jump neural networks with partially unknown transition probabilities. Firstly, a unified framework based on event-triggering approaches and quantization, a synchronization error model is first provided for discrete-time Markovian neural networks. Secondly, with the aid of Lyapunov-Krasovskii functional, sufficient conditions for the finite-time boundedness are deduced which can ensure synchronization of the system model, and the quantized finite-time controller and event-triggered parameters are co-designed so that Markovian neural networks are finite-time bounded by variable decoupled techniques. Finally, a simulation example is established to illustrate the availability of the presented methods.MSC Classification: 15A39 , 24D06 , 93B51 , 93D30 , 93E15

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“…As technology advances, cyber security has become a major concern, as illustrated by [29]. Different types of cyber-attacks are being discussed [30]- [32].…”

Section: Introductionmentioning

confidence: 99%

Adaptive event-triggered control for Markov jump systems under hybrid cyber-attacks and its application

Ali

Rajchakit

et al. 2022

Preprint

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This article investigates event-triggered control (ETC) of discrete-time Markov jump systems (MJSs) subject to hybrid cyber-attacks and unmeasured states. To reduce data transmission load, a mode-dependent event-triggered mechanism (ETM) has been developed. The unmeasured states are estimated using a Luenberger observer. Here deception attacks and DoS attacks are addressed. It is anticipated that input control signals sent across a network are vulnerable to cyber-attacks, in which adversaries could inject fake data into the control signals. So to secure the closed-loop system, sufficient conditions are derived. The solution of set matrix inequalities is used to build observer and controller gains. Finally, inverted pendulum applications are presented to illustrate the effectiveness and feasibility of the obtained theoretical results.

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Novel fuzzy event-triggered adaptive control for nonlinear systems with input hysteresis

Cited by 8 publications

References 36 publications

Fuzzy membership function-dependent switched control for nonlinear systems with memory sampled-data information

Fuzzy membership function-dependent switched control for nonlinear systems with memory sampled-data information

Event-based finite-time quantized synchronization for discrete Markovian neural networks

Adaptive event-triggered control for Markov jump systems under hybrid cyber-attacks and its application

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