Finite-time synchronization for periodic T–S fuzzy master-slave neural networks with distributed delays

Liu, Chang; Guo, Yuru; Rao, Hongxia; Lin, Ming; Xu, Yong

doi:10.1016/j.jfranklin.2020.12.030

Cited by 15 publications

(11 citation statements)

References 36 publications

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“…An adaptive T-S fuzzy control methodology is proposed to solve the synchronization problem of FO T-S fuzzy chaotic uncertain delayed systems by Wu et al in [18]. The authors of [19], have designed a finite time fuzzy controller for synchronization for a class of T-S fuzzy complex delayed neural. In [20], base on T-S fuzzy model and LMI method, a novel controller is proposed to synchronize the hyperchaotic systems with application in secure communication for non-ideal channels.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of T-S fuzzy methodology for guaranteed cost synchronization of different non-integer-order complex systems

Berardehi,

Zhang,

Taheri

et al. 2024

Preprint

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Recently, guaranteed cost synchronization of different complex dynamical systems is one of the main issues in engineering which has a lot of applications in applied sciences. In this work, a T-S fuzzy model-free state feedback (MFSF) methodology is designed for the synchronization of different chaotic fractional-order systems (FOSs) with input saturation. Based on the non-integer version of the Lyapunov stability theorem and linear matrix inequality concept, a T-S fuzzy model-free state feedback method is driven to suppress the undesired behavior of the FOSs without any unpleasant chattering phenomenon. Besides, by operating the proposed technique on synchronization of two chaotic FOSs, the effectiveness and applicability of the T-S fuzzy MFSF are validated.

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Section: Literature Reviewmentioning

confidence: 99%

Design of T-S fuzzy methodology for guaranteed cost synchronization of different non-integer-order complex systems

Berardehi,

Zhang,

Taheri

et al. 2024

Preprint

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“…Taking the derivative of 􏽥 V(t) along the trajectories of system (3), applying Lemma 3 and inequalities (21)(22)(23)(24)(25), one obtains 8…”

Section: Advances In Fuzzy Systemsmentioning

confidence: 99%

“…at is because nite-time synchronization has better robustness and anti-interference ability. Hence, many scholars are interested in the nite-time synchronization of fuzzy neural networks and have studied it extensively [13,[20][21][22][23][24][25][26]. Abdurahman et al [20] and Wang [21] investigated the nite-time synchronization problem of FCNNs with time-varying delays or time-varying coecients and proportional delays based on the nite-time stability theory, and inequality techniques, and some criteria of finite-time synchronization for the addressed network are derived.…”

Section: Introductionmentioning

confidence: 99%

Synchronization in Finite Time of Fuzzy Neural Networks with Hybrid Delays and Uncertain Nonlinear Perturbations

Zhao

2022

Advances in Fuzzy Systems

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This paper deals with the finite-time synchronization problem of a class of fuzzy neural networks with hybrid delays and uncertain nonlinear perturbations. By applying the famous finite-time stability theory, combining differential inequality techniques, and the analysis approach, several new algebraic sufficient criteria are obtained to realize finite-time synchronization between the drive system and the response system by designing a state feedback controller and an adaptive controller. Taking discrete delays, distributed delays, and uncertain nonlinear perturbations into account in fuzzy cellular neural networks makes the neural system more general than most existing cellular neural networks. Two different novel types of controllers designed to achieve finite-time synchronization can not only effectively overcome the influence of time delays and perturbations but also change their form according to the change of system state or perturbation to achieve a better control effect. Meanwhile, some algebraic sufficient criteria obtained in this paper can be proved by the parameters of the system itself, and the complex calculation of matrix inequality is avoided. Finally, the validity of our proposed results is confirmed by several examples and simulations. Furthermore, a secure communication problem is presented to further illustrate the fact of the obtained results.

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“…An adaptive T-S fuzzy control methodology is proposed to solve the synchronization problem of FO T-S fuzzy chaotic uncertain delayed systems by Wu et al (2021). Liu et al (2021) have designed a finite-time fuzzy controller for synchronization for a group of T-S fuzzy riotous delayed neural networks. Moreover, in the work by Wang et al (2021a, 2021b), novel sampled-data-based T-S fuzzy methods are designed to synchronize and control of Markovian jump systems and complex networked systems.…”

Section: Introductionmentioning

confidence: 99%

Implementation of T-S fuzzy approach for the synchronization and stabilization of non-integer-order complex systems with input saturation at a guaranteed cost

Berardehi

Zhang

Taheri

et al. 2023

Transactions of the Institute of Measurement and Control

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One of the fundamental topics in engineering that has many applications in applied sciences is guaranteed cost control and synchronization of chaotic dynamical systems. In this paper, a Takagi–Sugeno (T-S) fuzzy model-free state feedback (MFSF) technique is used to synchronize and stabilize various complex fractional-order systems (FOSs) in the presence of input saturation. A T-S fuzzy MFSF method is developed to suppress the inappropriate behavior of the FOSs without any unpleasant chattering phenomena using the non-integer form of the Lyapunov stability theory (LST) and linear matrix inequality concept. The suggested MFSF technique may be used to stabilize and synchronize FOSs with model uncertainty, external disturbances, and input saturation. The designed method has the advantages of being a model-free technique, being resistant to uncertainties, being simple to use, and achieving the equilibrium point quickly at a guaranteed cost. Furthermore, the effectiveness and application of the T-S fuzzy MFSF are proven using the suggested approach to synchronize two separate canonical chaotic FOSs and stabilize the permanent magnet synchronous motor (PMSM) chaotic system. Two comparisons demonstrate the efficacy and advantages of the T-S fuzzy approach over other existing control methods.

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Finite-time synchronization for periodic T–S fuzzy master-slave neural networks with distributed delays

Cited by 15 publications

References 36 publications

Design of T-S fuzzy methodology for guaranteed cost synchronization of different non-integer-order complex systems

Design of T-S fuzzy methodology for guaranteed cost synchronization of different non-integer-order complex systems

Synchronization in Finite Time of Fuzzy Neural Networks with Hybrid Delays and Uncertain Nonlinear Perturbations

Implementation of T-S fuzzy approach for the synchronization and stabilization of non-integer-order complex systems with input saturation at a guaranteed cost

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