Adaptive sliding mode synchronisation for fractional‐order non‐linear systems in the presence of time‐varying actuator faults

Bataghva, Meysam; Hashemi, Mahnaz

doi:10.1049/iet-cta.2017.0458

Cited by 34 publications

(20 citation statements)

References 38 publications

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“…The derivative‐integral operator is expressed as

D_{t}^{α}

, consisting of differentiation, and integration operator is applied generally in the fractional derivative and integral calculus explained in Reference :

D_{t}^{α} = \{\begin{matrix} left \\ \frac{d^{α}}{{dt}^{α}} α > 0, \\ 1 α = 0, \\ \int_{0}^{t} {((), italicd τ)}^{- α} α < 0 . \end{matrix}

…”

Section: System Description and Preparationsmentioning

confidence: 99%

Approximation‐based adaptive fault compensation backstepping control of fractional‐order nonlinear systems: An output‐feedback scheme

Naderolasli

Hashemi

Shojaei

2020

Adaptive Control & Signal

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Summary An observer‐based adaptive fuzzy backstepping approach is proposed for nonlinear systems with respect to fractional‐order differential equations, unmatched uncertainties, unmeasured states, and actuator faults. The approximation capability of fuzzy logic system and minimal learning parameter approaches are applied to identify uncertain functions in a simultaneous manner. For estimating the unavailable conditions, a fuzzy fractional‐order state‐observer is extended. Applying fault‐tolerant approach in a backstepping design methodology would provide a new fault‐tolerant adaptive fuzzy output‐feedback approach for fractional‐order strict‐feedback systems. This control structure would assure the considered system stability through selection of the appropriate Lyapunov candidate function. Two numerical simulations are run to exhibit the validity herein.

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“…The derivative‐integral operator is expressed as

D_{t}^{α}

, consisting of differentiation, and integration operator is applied generally in the fractional derivative and integral calculus explained in Reference :

D_{t}^{α} = \{\begin{matrix} left \\ \frac{d^{α}}{{dt}^{α}} α > 0, \\ 1 α = 0, \\ \int_{0}^{t} {((), italicd τ)}^{- α} α < 0 . \end{matrix}

…”

Section: System Description and Preparationsmentioning

confidence: 99%

Approximation‐based adaptive fault compensation backstepping control of fractional‐order nonlinear systems: An output‐feedback scheme

Naderolasli

Hashemi

Shojaei

2020

Adaptive Control & Signal

Self Cite

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“…This problem can be solved using the variable change X i = P 01 L i and Y i = P 02 K i . Applying the Schur complement, we can obtain the LMI form (29). So if J 1 (t) < 0, the error dynamics system (25) is stable satisfying the H ∞ performance (28).…”

Section: Stability Analysismentioning

confidence: 99%

“…The problem of both actuator and sensor FE under one unified framework for T-S fuzzy systems is investigated. Whereas, many researchers have considered only sensor faults [25][26][27] or actuator faults [28,29]. 4.…”

Section: Introductionmentioning

confidence: 99%

Robust Static Output-Feedback Fault Tolerant Control for a Class of T-S Fuzzy Systems using Adaptive Sliding Mode Observer Approach

Dhahri¹,

Alaïa²

2020

Adv. sci. technol. eng. syst. j.

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In this paper, the problems of actuator and sensor fault estimation (FE) and fault-tolerant control (FTC) for uncertain nonlinear systems represented by Takagi-Sugeno (T-S) fuzzy models are investigated. First, a robust fuzzy adaptive sliding mode observer (SMO) is designed to simultaneously estimate system states and both actuator and sensor faults. Then, using the obtained on-line FE information, a static output-feedback fault-tolerant control (SOFFTC) is developed to compensate the fault effects and stabilize the closed-loop system. Moreover, sufficient conditions for the existence of the proposed observer and controller are given in terms of linear matrix inequalities (LMIs). The robustness against uncertainties is treated using the H ∞ optimization technique to attenuate its effect on the estimation error. Finally, the simulation results of nonlinear inverted pendulum with cart system validate the efficiency of the proposed method.

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“…Meanwhile, in [23], Jiang et al have also studied the robust H ∞ problem for the Markovian jump systems with non-linear perturbations via SMC. Furthermore, SMC approach has been widely applied in various theoretical and practical aspects, such as stochastic systems [24,25], discrete systems [26,27], exponential stabilisation [28,29], synchronisation [30,31], switched systems [32] and so on. Moreover, selecting an approximation function to ensure the robustness against the system uncertainty has been applied to SMC in [33].…”

Section: Introductionmentioning

confidence: 99%

Projective synchronisation of variable‐order systems via fractional sliding mode control approach

Meng

Kao

Gao

et al. 2020

IET Control Theory & Applications

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Adaptive sliding mode synchronisation for fractional‐order non‐linear systems in the presence of time‐varying actuator faults

Cited by 34 publications

References 38 publications

Approximation‐based adaptive fault compensation backstepping control of fractional‐order nonlinear systems: An output‐feedback scheme

Approximation‐based adaptive fault compensation backstepping control of fractional‐order nonlinear systems: An output‐feedback scheme

Robust Static Output-Feedback Fault Tolerant Control for a Class of T-S Fuzzy Systems using Adaptive Sliding Mode Observer Approach

Projective synchronisation of variable‐order systems via fractional sliding mode control approach

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