Reduced‐order fractional integral observer for synchronisation and anti‐synchronisation of fractional‐order chaotic systems

Meléndez‐Vázquez, Fidel; Martínez-Guerra, Rafael

doi:10.1049/iet-cta.2017.1117

Cited by 10 publications

(3 citation statements)

References 44 publications

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“…The investigation of chaos in fractional-order systems has attracted the attention of researchers [5,6]. The problem of chaos synchronization, fractional-order PID control, sliding mode control, adaptive control, and non-linear observers have been studied widely in the literature [7,8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive observer‐based control for fractional‐order chaotic MEMS system

Yaghoubi

Adeli

2023

IET Control Theory & Appl

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This paper proposes an adaptive observer‐based scheme to control fractional‐order chaotic micro‐electro‐mechanical‐systems (MEMS). The fractional‐order model of a system exhibits the dynamic behaviour of a physical system more accurately than a similar integer‐order one. An observer‐based control considering the unknown parameters is presented for a fractional‐order chaotic micro‐electro‐mechanical‐system. To handle the existence of the unknown parameters, the control law consists of both estimated parameters and also estimated states that have been designed. First, a fractional‐order observer is designed to estimate the state variables. The asymptotic convergence to zero of the estimation state error is proved using the Lyapunov scheme. After that, with the designed observer as a foundation, adaptive control is expanded for the closed‐loop system to ensure its asymptotic stability. An evaluation of the proposed method shows its efficacy via simulations for two case studies.

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Section: Introductionmentioning

confidence: 99%

Adaptive observer‐based control for fractional‐order chaotic MEMS system

Yaghoubi

Adeli

2023

IET Control Theory & Appl

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“…As a consequence, the Lyapunov–Krasovskii‐based stability conditions in integer‐order time‐delay systems can result in stability of their fractional‐order counterparts. In [22], the problems of synchronisation and anti‐synchronisation are solved for commensurate and incommensurate fractional chaotic systems. A reduced‐order fractional integral observer is proposed for fractional systems satisfying a fractional algebraic observability condition, which is shown to be Mittag–Leffler stable.…”

Section: Introductionmentioning

confidence: 99%

Stability of conformable linear differential systems: a behavioural framework with applications in fractional‐order control

Mayo-Maldonado

Fernández‐Anaya

Ruiz-Martinez

2020

IET Control Theory & Appl

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“…Control problem for fractional order multiagent systems were introduced in [8][9][10]. Dynamic properties, control, and synchronization of fractional order chaotic systems were discussed in [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Robust Stabilization of Nonlinear Fractional Order Interconnected Systems Based on T-S Fuzzy Model

2020

Complexity

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This paper concerns robust stabilization of nonlinear fractional order interconnected systems. Based on uncertain fractional order Takagi–Sugeno fuzzy model and the fractional order extension of lyapunov direct method, a parallel distributed compensate controller is designed to asymptotically stabilize the fractional order interconnected systems. Then, a sufficient condition is given in the format of linear matrix inequalities. Simulation example is given to validate the effectiveness of the approach.

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Reduced‐order fractional integral observer for synchronisation and anti‐synchronisation of fractional‐order chaotic systems

Cited by 10 publications

References 44 publications

Adaptive observer‐based control for fractional‐order chaotic MEMS system

Adaptive observer‐based control for fractional‐order chaotic MEMS system

Stability of conformable linear differential systems: a behavioural framework with applications in fractional‐order control

Robust Stabilization of Nonlinear Fractional Order Interconnected Systems Based on T-S Fuzzy Model

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