Fuzzy synchronization of chaotic systems via intermittent control

Yuan-gan, Wang; Yu, Hong-Lin

doi:10.1016/j.chaos.2017.11.024

Cited by 37 publications

(20 citation statements)

References 29 publications

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“…Shukla and Sharma designed a backstepping controller and analyzed the stability of the designed controller for a class of three-dimensional chaotic systems [ 24 ]. To name just a few, fuzzy controller [ 25 , 26 , 27 , 28 , 29 ], sliding mode controller [ 30 , 31 , 32 , 33 , 34 ], and hybrid controllers [ 35 , 36 , 37 , 38 , 39 ] are some other controllers that are implemented to control and synchronize the chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Entropy Analysis and Neural Network-Based Adaptive Control of a Non-Equilibrium Four-Dimensional Chaotic System with Hidden Attractors

Jahanshahi

Shahriari-kahkeshi

Alcaraz

et al. 2019

Entropy

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Today, four-dimensional chaotic systems are attracting considerable attention because of their special characteristics. This paper presents a non-equilibrium four-dimensional chaotic system with hidden attractors and investigates its dynamical behavior using a bifurcation diagram, as well as three well-known entropy measures, such as approximate entropy, sample entropy, and Fuzzy entropy. In order to stabilize the proposed chaotic system, an adaptive radial-basis function neural network (RBF-NN)–based control method is proposed to represent the model of the uncertain nonlinear dynamics of the system. The Lyapunov direct method-based stability analysis of the proposed approach guarantees that all of the closed-loop signals are semi-globally uniformly ultimately bounded. Also, adaptive learning laws are proposed to tune the weight coefficients of the RBF-NN. The proposed adaptive control approach requires neither the prior information about the uncertain dynamics nor the parameters value of the considered system. Results of simulation validate the performance of the proposed control method.

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Section: Introductionmentioning

confidence: 99%

Entropy Analysis and Neural Network-Based Adaptive Control of a Non-Equilibrium Four-Dimensional Chaotic System with Hidden Attractors

Jahanshahi

Shahriari-kahkeshi

Alcaraz

et al. 2019

Entropy

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“…Two chaotic systems synchronization of the main idea is to design a suitable controller, to make the state of the slave system track the status of the master system. In order to solve the synchronization problem of chaotic systems, many methods have been proposed, such as feedback control method [13,14], continuous sliding mode method [15,16], and fuzzy control method [17,18]. Among them, the fuzzy control has attracted much attention because of its wide application and independent of the precise mathematical model of the controlled object and so on.…”

Section: Introductionmentioning

confidence: 99%

Improved T‐S Fuzzy Control for Uncertain Time‐Delay Coronary Artery System

et al. 2019

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This paper is based on the Takagi-Sugeno (T-S) fuzzy models to construct a coronary artery system (CAS) T-S fuzzy controller and considers the uncertainties of system state parameters in CAS. We propose the fuzzy model of CAS with uncertainties. By using T-S fuzzy model of CAS and the use of parallel distributed compensation (PDC) concept, the same fuzzy set is assigned to T-S fuzzy controller. Based on this, a PDC controller whose fuzzy rules correspond to the fuzzy model is designed. By constructing a suitable Lyapunov-Krasovskii function (LKF), the stability conditions of the linear matrix inequality (LMI) are exported. Simulation results show that the method proposed in this paper is correct and effective and has certain practical significance.

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“…In recent years, chaotic dynamic systems have been widely investigated by researchers; see, for instance, [1][2][3][4][5][6][7][8][9][10][11][12] and the references therein. Very often, chaos in many dynamic systems is an origin of the generation of oscillation and an origin of instability.…”

Section: Introductionmentioning

confidence: 99%

Chaos Suppression and Stabilization of Generalized Liu Chaotic Control System

Sun¹,

Hsieh²

2018

IJTSRD

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In this paper, the concept of generalized stabilization for nonlinear systems is introduced and the stabilization of the generalized Liu chaotic control system is explored. Based on the time approach with differential inequalities, a suitable control is presented such that the generalized stabilization for a class of Liu chaotic system can be achieved. Meanwhile, not only the guaranteed exponential convergence rate can be arbitrarily pre specified but also the critical time can be correctly estimated. Finally, some numerical simulations are given to demonstrate the feasibility and effectiveness of the obtained results.

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Fuzzy synchronization of chaotic systems via intermittent control

Cited by 37 publications

References 29 publications

Entropy Analysis and Neural Network-Based Adaptive Control of a Non-Equilibrium Four-Dimensional Chaotic System with Hidden Attractors

Entropy Analysis and Neural Network-Based Adaptive Control of a Non-Equilibrium Four-Dimensional Chaotic System with Hidden Attractors

Improved T‐S Fuzzy Control for Uncertain Time‐Delay Coronary Artery System

Chaos Suppression and Stabilization of Generalized Liu Chaotic Control System

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