Synchronization of Unknown Uncertain Chaotic Systems Via Adaptive Control Method

Aghababa, Mohammad Pourmahmood; Hashtarkhani, Bijan

doi:10.1115/1.4027976

Cited by 12 publications

(7 citation statements)

References 34 publications

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“…In this example, two different pairs of the chaotic systems (electromechanical device and electrostatic transducer) with different initial conditions are considered. An electromechanical device as a master system is presented as follows (Aghababa and Hashtarkhani, 2015)…”

Section: Simulation Resultsmentioning

confidence: 99%

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Chaos synchronization using an improved type-2 fuzzy wavelet neural network with application to secure communication

Zirkohi

Shoja-Majidabad

2021

Journal of Vibration and Control

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In this article, a simple yet efficient adaptive control method is proposed to investigate synchronizing two chaotic systems. This approach presents an improved type-2 fuzzy wavelet neural network for estimating the unknown terms and the external disturbance in the chaotic systems’ dynamics. Furthermore, an efficient robust control term is integrated into the suggested controller so that the robustness of the controller against system unknown disturbances and uncertainties is improved. This approach offers a very desirable characteristic as a model-free controller. With the help of the Lyapunov stability theory supported with the transient performance analysis, it is established that the proposed control scheme can guarantee the synchronization and the stability of the closed-loop control system. Comparative simulation results with radial basis function neural networks are given that prove the proposed method has superiority in secure communication applications.

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Section: Simulation Resultsmentioning

confidence: 99%

“…An electrostatic transducer as a slave system is also presented as follows (Aghababa and Hashtarkhani, 2015)where u1, u2, u3, and u4 are the control inputs to be determined using the proposed controller designed in (19). The chaotic systems (57) and (58) can be easily rewritten in the form of (3) and (4) as follows…”

Section: Simulation Resultsmentioning

confidence: 99%

Chaos synchronization using an improved type-2 fuzzy wavelet neural network with application to secure communication

Zirkohi

Shoja-Majidabad

2021

Journal of Vibration and Control

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“…Some other efforts related to the chaos control can be found in Refs. [17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Centralized Approach to Control Chaotic and Hyperchaotic Dynamics of Smart Valves Network

Ashrafiuon

Ayoubi

2017

Journal of Computational and Nonlinear Dynamics

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Catastrophic chaotic and hyperchaotic dynamical behaviors have been experimentally observed in the so-called “smart valves” network, given certain critical parameters and initial conditions. The centralized network-based control of these coupled systems may effectively mitigate the harmful dynamics of the valve-actuator configuration which can be potentially caused by a remote set and would gradually affect the whole network. In this work, we address the centralized control of two bi-directional solenoid actuated butterfly valves dynamically coupled in series subject to the chaotic and hyperchaotic dynamics. An interconnected adaptive scheme is developed and examined to vanish both the chaotic and hyperchaotic dynamics and return the coupled network to its safe domain of operation.

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“…Then an adaptive observer was designed to synchronize the states of the master and slave oscillators using a single scalar signal corresponding to an observable state variable of the driving oscillator. Aghababa and Hashtarkhani [36] addressed the issue of synchronizing two different uncertain chaotic systems with unknown and different bounds via adaptive control method. Nijmeijer and Mareels [37] reformulated the chaotic synchronization as an observer design problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal targeting of nonlinear chaotic systems using a novel evolutionary computing strategy

Wang¹,

Lyu

et al. 2016

Knowledge-Based Systems

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Control of chaotic systems to given targets is a subject of substantial and well-developed research issue in nonlinear science, which can be formulated as a class of multi-modal constrained numerical optimization problem with multi-dimensional decision variables. This investigation elucidates the feasibility of applying a novel population-based metaheuristics labelled here as Teaching-learning-based optimization to direct the orbits of discrete chaotic dynamical systems towards the desired target region. Several consecutive control steps of small bounded perturbations are made in the Teaching-learning-based optimization strategy to direct the chaotic series towards the optimal neighborhood of the desired target rapidly, where a conventional controller is effective for chaos control. Working with the dynamics of the well-known Hénon as well as Ushio discrete chaotic systems, we assess the effectiveness and efficiency of the Teaching-learning-based optimization based optimal control technique, meanwhile the impacts of the core parameters on performances are also discussed.Furthermore, possible engineering applications of directing chaotic orbits are discussed. Keywords:Chaos control; chaotic dynamics; optimization; computational intelligence;Teaching-learning-based optimization Highlights► Control of chaotic systems is formulated as constrained optimization problem.► Teaching-learning-based optimization is to direct the chaotic series.► The efficacy of TLBO based optimal control technique have been demonstrated.

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Synchronization of Unknown Uncertain Chaotic Systems Via Adaptive Control Method

Cited by 12 publications

References 34 publications

Chaos synchronization using an improved type-2 fuzzy wavelet neural network with application to secure communication

Chaos synchronization using an improved type-2 fuzzy wavelet neural network with application to secure communication

An Adaptive Centralized Approach to Control Chaotic and Hyperchaotic Dynamics of Smart Valves Network

Optimal targeting of nonlinear chaotic systems using a novel evolutionary computing strategy

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