Two methods for terminal sliding‐mode synchronization of fractional‐order nonlinear chaotic systems

Bei-xing, Mao

doi:10.1002/asjc.2328

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Although the structure of the traditional sliding mode surface is simple, the system state asymptotically converges to the equilibrium point, which means that the convergence time is infinite, which is not conducive to engineering applications. Therefore, some scholars introduce finite-time convergence into sliding surface and form the concept of terminal sliding mode (TSM) (Mao, 2021; Shi et al, 2022). Fractional-order operators have been applied in sliding mode controllers to form fractional-order SMC (FSMC), which have been proven to suppress chattering more effectively than traditional integer-order SMC.…”

Section: Introductionmentioning

confidence: 99%

Fixed-time convergence of second-order nonlinear systems based on nonsingular fractional sliding mode

Lei,

Lan,

Sun

et al. 2024

Transactions of the Institute of Measurement and Control

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In this paper, a fixed-time convergence scheme for fractional sliding modes is proposed for nonlinear second-order systems. Firstly, a fixed-time fractional-order sliding mode surface is designed by combining the fixed-time theory with fractional-order sliding mode control, which has a faster convergence rate and less chattering. Secondly, the proposed sliding mode controller is applied to a class of second-order nonlinear systems subject to uncertainties and external perturbations to ensure that the system is globally robust fixed-time stable. Then, a continuous fractional order approach law is designed and the proposed sliding mode controller is shown to converge in fixed time by Lyapunov function and the convergence time is related to the choice of controller parameters. Finally, the fixed-time fractional-order sliding mode control strategy is applied to a second-order nonlinear magnetic levitation system system, and the simulation results verify the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Fixed-time convergence of second-order nonlinear systems based on nonsingular fractional sliding mode

Lei,

Lan,

Sun

et al. 2024

Transactions of the Institute of Measurement and Control

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“…In many practical applications, the precise dynamics of the system cannot be obtained, and it is vulnerable to external disturbances. Therefore, many sliding-mode control schemes with better robustness are used for the synchronisation of fractional-order hyperchaotic systems [12], such as adaptive sliding mode control [13,14], terminal sliding mode control [15][16][17][18], improved sliding mode control [19][20][21], fixed-time sliding mode control [22], neural network sliding mode control [23] etc.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks

et al. 2022

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To solve the synchronisation problem associated with fractional‐order hyperchaotic systems, in this study, a new dual‐neural network finite‐time sliding mode control method was developed, and a differential evolution algorithm was used to optimise the switching gain, control parameters, and sliding mode surface parameters, greatly reducing chattering problems in sliding mode controllers. By using the developed method, the complete synchronisation of the drive system and the response system of a fractional‐order hyperchaotic system was realised in a finite time; moreover, the stability of the error system under this method was proved by using Lyapunov stability theorem. Numerical simulation results verified the feasibility and superiority of the method.

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“…In [17], a fractional order adaptive synchronization controller was proposed for a new four-scroll chaotic systems. In [18], the authors studied adaptive terminal sliding mode synchronization for fractional chaotic systems with uncertainty and nonlinearity. In [19], the authors studied the fractional order chaotic systems with randomly occurring uncertainties and proposed a feedback controller based on LMI control to reach chaos synchronization.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Adaptive Neural Network Backstepping Sliding Mode Control for Fractional Order Chaotic Systems Synchronization With Input Delay

2021

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This paper investigates the fractional order chaotic systems synchronization with input delay. To reduce the utilization of communication resources and achieve system synchronization, an adaptive neural network backstepping sliding mode controller is proposed based on event-triggered scheme without Zeno behavior. To avoid ''explosion of complexity'' and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. The sliding term is introduced to enhance robustness. The Pade delay approximation method is used to handle the input delay, which can reduce the analysis complexity of fractional order chaotic systems with input delay. The unknown nonlinear functions and uncertain disturbances are approximated by the RBF neural network. An observer is used for state estimation of the fractional order system. By applying the Lyapunov stability theory, we can prove that the all closed-loop signals are bounded. Examples and simulations prove the feasibility of the proposed control method.INDEX TERMS Fractional order chaotic systems synchronization, event-triggered, dynamic surface control, neural network, observer, input delay.

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Two methods for terminal sliding‐mode synchronization of fractional‐order nonlinear chaotic systems

Cited by 5 publications

References 20 publications

Fixed-time convergence of second-order nonlinear systems based on nonsingular fractional sliding mode

Fixed-time convergence of second-order nonlinear systems based on nonsingular fractional sliding mode

Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks

Event-Triggered Adaptive Neural Network Backstepping Sliding Mode Control for Fractional Order Chaotic Systems Synchronization With Input Delay

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