Fixed-Time Stabilization for IT2 T–S Fuzzy Interconnected Systems via Event-Triggered Mechanism: An Exponential Gain Method

Rong, Nannan; Wang, Zhanshan

doi:10.1109/tfuzz.2019.2904192

Cited by 35 publications

(15 citation statements)

References 46 publications

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“…Reference 21 proposed a dynamic event‐triggered control law for a class of reaction‐diffusion systems with Robin actuation. In recent years, some researchers applied the ETM to interconnected systems (see References 22‐25). For instance, in order to stabilize an interconnected system composed of nonlinear subsystems, Reference 22 developed an optimal control policy based neural networks under adaptive event‐triggering mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in order to stabilize an interconnected system composed of nonlinear subsystems, Reference 22 developed an optimal control policy based neural networks under adaptive event‐triggering mechanism. Reference 24 designed a novel static event‐triggering mechanism for IT2 T–S fuzzy interconnected systems by introducing an exponential term. However, time delays have not been considered in these works.…”

Section: Introductionmentioning

confidence: 99%

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Event‐triggered stabilization for large‐scale interconnected systems with time delays

Kang

Liu

2022

Intl J Robust & Nonlinear

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This article addresses the event-triggered stabilization for large-scale interconnected systems in presence of time delays. Two types of event-triggering mechanisms (static event-triggering mechanism and dynamic event-triggering mechanism) are constructed. For each case, event-triggered predictor-based controller is designed to compensate time delays by using backstepping method. Via Lyapunov approach, constructive exponential stability conditions are established in the framework of linear matrix inequalities (LMIs). A numerical simulation example is given to demonstrate the effectiveness of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event‐triggered stabilization for large‐scale interconnected systems with time delays

Kang

Liu

2022

Intl J Robust & Nonlinear

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“…Different from T-S model, IT2 T-S model proposed by [9] has the superiority of providing better control performance in the process of solving parameters uncertainties. Therefore, several works on IT2 T-S model have been obtained, for example, see [10][11][12][13][14][15][16]. Among them, the researchers in [10][11][12] studied the state-feedback control design and stability analysis for the continuous IT2 fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered fuzzy output feedback fault‐tolerant control for interval type‐2 Takagi–Sugeno large‐scale systems with time delays

LIU

Yang

2022

IET Control Theory & Appl

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The event‐triggered output feedback fault‐tolerant control problem is studied in this paper for interval type‐2 Takagi–Sugeno (IT2 T‐S) fuzzy large‐scale systems with time delays. First, a fuzzy state observer is designed to address the immeasurable states problem. Then, by introducing a discrete event‐triggered mechanism, an observer‐based event‐triggered fuzzy decentralized controller with dynamic parameters is synthesized in the presence of actuator faults based on the nonparallel distribution compensation (PDC) design principle. The sufficient conditions of asymptotic stability for the closed‐loop systems are derived by constructing an appropriate dynamic parameter based Lyapunov–Krasovskii functional candidate. Finally, a simulation example is provided to verify the feasibility of the developed fuzzy control method.

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“…Based on this framework, the robust control theory and technology is applied in linear systems to the design of complex nonlinear systems. Hence, many significant consequences about the T-S fuzzy models have been published, such as [15][16][17][18] and the references therein. In [19], through switching the fuzzy model, the relaxed stability criterion of T-S fuzzy control systems was studied.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy filtering‐based fault detection for a class of discrete‐time conic‐type nonlinear systems

Wang

2021

IET Signal Processing

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The authors investigates the problem of fuzzy fault detection filter (FFDF) design for a class of discrete-time conic-type nonlinear systems. By applying Takagi-Sugeno fuzzy models, the conic-type dynamic FFDF system is established. Then, utilizing the Lyapunov function method to find a sufficient condition which ensures that the conic-type dynamic FFDF system is asymptotically stable. After that, using linear matrix inequalities techniques, the FFDF design problem is transformed into an optimization algorithm. Finally, the simulation results demonstrate that the designed FFDF is effective for detecting the faults. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Fixed-Time Stabilization for IT2 T–S Fuzzy Interconnected Systems via Event-Triggered Mechanism: An Exponential Gain Method

Cited by 35 publications

References 46 publications

Event‐triggered stabilization for large‐scale interconnected systems with time delays

Event‐triggered stabilization for large‐scale interconnected systems with time delays

Event‐triggered fuzzy output feedback fault‐tolerant control for interval type‐2 Takagi–Sugeno large‐scale systems with time delays

Fuzzy filtering‐based fault detection for a class of discrete‐time conic‐type nonlinear systems

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