Dynamical analysis and preassigned-time intermittent control of memristive chaotic system via T–S fuzzy method

In this paper, we propose a novel fourth-order memristive chaotic system (MCS), in which both its dynamical behaviors and the preassigned-time stabilization problem are analyzed. First, the dynamical behaviors of the proposed MCS are studied in detail, such as the infinite unstable equilibrium points, the chaotic attractor, the Lyapunov exponents, the Kaplan-Yorke dimension, and the bifurcation. Then, the T-S fuzzy method is employed to characterize the MCS, and a simpler model is built to deal with the nonlinearity caused by the memristor in the MCS. In addition, two intermittent controllers are proposed to guarantee the preassigned-time stability and the settling time, which can be set freely, independent of system parameters and initial state. Finally, numerical simulations provide solid confirmation for the validity of these theoretical results.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2025-02-01
DOI
https://doi.org/10.1063/5.0221159
Citations
14
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
20.82

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article

Dynamical analysis and preassigned-time intermittent control of memristive chaotic system via T–S fuzzy method

Wudi Wen, Yan Li, Guodong Zhang, Leimin Wang et al.
14 citations
Chaos An Interdisciplinary Journal of Nonlinear Science
Neural Networks Stability and Synchronization
20.82
article

Dynamical analysis and preassigned-time intermittent control of memristive chaotic system via T–S fuzzy method

Wudi Wen, Yan Li, Guodong Zhang, Leimin Wang, Qiming Wang
article en
14 citations

Abstract

In this paper, we propose a novel fourth-order memristive chaotic system (MCS), in which both its dynamical behaviors and the preassigned-time stabilization problem are analyzed. First, the dynamical behaviors of the proposed MCS are studied in detail, such as the infinite unstable equilibrium points, the chaotic attractor, the Lyapunov exponents, the Kaplan-Yorke dimension, and the bifurcation. Then, the T-S fuzzy method is employed to characterize the MCS, and a simpler model is built to deal with the nonlinearity caused by the memristor in the MCS. In addition, two intermittent controllers are proposed to guarantee the preassigned-time stability and the settling time, which can be set freely, independent of system parameters and initial state. Finally, numerical simulations provide solid confirmation for the validity of these theoretical results.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 35(2)
Minzu University of China (CN), Huazhong Agricultural University (CN), Naval University of Engineering (CN), China University of Geosciences (CN), Shandong Institute of Automation (CN), Intelligent Automation (United States) (US)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
Neural Networks Stability and Synchronization
20.82
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