A fixed-time robust controller based on zeroing neural network for generalized projective synchronization of chaotic systems

Xiao, Lin; Li, Linju; Cao, Penglin; He, Yongjun

doi:10.1016/j.chaos.2023.113279

Cited by 14 publications

(6 citation statements)

References 37 publications

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“…Subsequently, they devised a superior controller to facilitate synchronization of chaotic systems within a predetermined timeframe. Finally, the researchers proposed a novel activation function to alleviate chattering phenomena, and experimentally validated the effectiveness of the proposed model [111]. the master chaotic system can be described as follows:…”

Section: Application In Chaotic Systemmentioning

confidence: 99%

“…The precise details regarding the construction of the controller can be located in the citation [111].…”

Section: Application In Chaotic Systemmentioning

confidence: 99%

“…In 2023, Xiao et al presented a fixed-time robust controller (FXTRC) based on ZNN [111], addressing the Generalized Projective Synchronization of a class of chaotic systems. Additionally, they substantiated the fixed-time synchronization and robustness of FXTRC in controlling the Generalized Projective Synchronization of chaotic systems.…”

Section: Application In Chaotic Systemmentioning

confidence: 99%

See 2 more Smart Citations

Applications of Zeroing Neural Networks: A Survey

Wang,

Zhang,

Huang

et al. 2024

IEEE Access

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Time-varying problems are prevalent in engineering, presenting a significant challenge due to the fluctuations in parameters and goals at different time points. The Zeroing Neural Network (ZNN), a specialized form of Recurrent Neural Network (RNN) developed by Zhang et al., has gained attention for its rapid convergence speed and robustness making it a valuable tool for real-time solving of diverse timevarying problems. This review article explores the practical applications of ZNN across various domains in the past two decades, specifically focusing on robot manipulator path tracking, motion planning, and chaotic systems. The comprehensive scope of this review is essential for researchers and beginners looking to grasp the efficacy of ZNN in addressing practical challenges in diverse fields. INDEX TERMS time-varying problems, Zeroing neural network (ZNN),robustness,robot manipulator I. INTRODUCTION

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Section: Application In Chaotic Systemmentioning

confidence: 99%

“…The precise details regarding the construction of the controller can be located in the citation [111].…”

Section: Application In Chaotic Systemmentioning

confidence: 99%

Section: Application In Chaotic Systemmentioning

confidence: 99%

See 1 more Smart Citation

Applications of Zeroing Neural Networks: A Survey

Wang,

Zhang,

Huang

et al. 2024

IEEE Access

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“…Thus, in theory and application, studying FPS and AS are very important. Moreover, many results on FPS, PS and AS can be formed in literature for the integer-order NN systems [28][29][30][31]. The Mittag-Leffler function is a generalization of the exponential function for fractional-order calculus.…”

Section: Introductionmentioning

confidence: 99%

Function projective Mittag-Leffler synchronization of non-identical fractional-order neural networks

Baluni,

Yadav,

Das

et al. 2024

Phys. Scr.

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This article investigates the function projective Mittag-Leffler synchronization (FPMLS) between non-identical fractional-order neural networks (FONNs).The stability analysis is carried out using an existing lemma for the Lyapunov function in the FONN systems. Based on the stability theorem of FONN, a non-linear controller is designed to achieve FPMLS. Moreover, global Mittag-Leffler synchronization (GMLS) is investigated in the context of other synchronization techniques, such as projective synchronization (PS), anti-synchronization (AS) and complete synchonization (CS). Using the definition of the Caputo derivative, the Mittag-Leffler function and the Lyapunov stability theory, some stability results for the FPMLS scheme for FONN are discussed. Finally, the proposed technique is applied to a numerical example to validate its efficiency and the unwavering quality of the several applied synchronization conditions.

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“…Since the discovery of chaotic oscillations in a nonlinear weather model by Lorenz in 1963, chaotic oscillators, their modelling, and control have received much attention in the literature [1]. Chaotic systems and chaotic maps are widely applied in engineering domains such as mechanical oscillations [6,7], robotics [8][9][10], nanosystem [11], lasers [12][13][14], nuclear reactor [15], neural networks [16], encryption [17,18], cryptosystems [19], and communication devices [20].…”

mentioning

confidence: 99%