Novel Neural Network Fractional-Order Sliding-Mode Control With Application to Active Power Filter

Fei, Juntao; Wang, Huan; Fang, Yunmei

doi:10.1109/tsmc.2021.3071360

Cited by 142 publications

(72 citation statements)

References 36 publications

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“…In [44], delayed signal cancellation is investigated for harmonic extraction under an adverse grid by adopting a generalized trigonometric function. In [45], a fractional-order sliding-mode control scheme based on a two-hidden-layer recurrent neural network (THLRNN) is proposed for a single-phase shunt active power filter. A novel switching pulse generation methodology based on adaptive fuzzy hysteresis current controlled hybrid shunt active power filter (A-F-HCC-HSAPF) is presented in [46].…”

Section: Fig 1 Block Diagram Of Sapfmentioning

confidence: 99%

Synchronization of Active Power Filter under Distorted Grid Conditions

Ullah

2021

IJRCS

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Advancement in technology has resulted in increased use of electrical energy. The trend of using energy-saving appliances is also increased considerably. Harmonic currents have been increased in power systems due to the large-scale use of nonlinear loads. Shunt Active Power Filter (SAPF) is the most widely used technique for the compensation of current harmonics. Under adverse grid conditions performance of SAPF is affected badly. In this paper, a modified synchronous reference frame theory is proposed for current reference generation by incorporating an advanced phase-locked loop technique for the estimation of frequency and phase. The proposed approach results in an accurate extraction of reference current in the presence of various grid disturbances. Matlab/Simulink environment is used for evaluating the performance. The results achieved show excellent performance of the proposed technique in terms of reducing harmonics distortion and dynamic response. The total harmonics distortion of the compensated source current is reduced to a value well within the limits of the IEEE-519 standard.

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Section: Fig 1 Block Diagram Of Sapfmentioning

confidence: 99%

Synchronization of Active Power Filter under Distorted Grid Conditions

Ullah

2021

IJRCS

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“…The development of NDOB on pendulum system can be found in [39]. Otherwise, some advanced disturbance compensations based on a neural network system were investigated in [40][41][42][43][44]. To simplify the procedure of the design of a disturbance observer, this study proposed a new DOB to scope the disturbance and uncertainty of a MEMS gyroscope under the conjunction of an unknown disturbance in exogenous form.…”

Section: Introductionmentioning

confidence: 99%

“…Lemma 2 is used to define the observer gains of Eq (40),. where the states observer gains are placed in the LMI region.…”

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confidence: 99%

Robust Observer Based on Fixed-Time Sliding Mode Control of Position/Velocity for a T-S Fuzzy MEMS Gyroscope

Giap

Nguyen

et al. 2021

IEEE Access

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“…Fuzzy control and neural networks have the ability to approximate unknown smooth functions and have been used in identification and control [8][9][10][11]. The adaptive approxi-Mathematics 2021, 9, 2124 2 of 20 mation of the lumped parameter uncertainty of the micro gyroscope model is realized by utilizing various neural networks [12,13], fuzzy system [14,15], and fuzzy neural network approaches [16][17][18][19]. The combination of sliding mode control and a neural algorithm is also used in an active power filter [20,21] and magnetic levitation system [22].…”

Section: Introductionmentioning

confidence: 99%

Multiple Loop Fuzzy Neural Network Fractional Order Sliding Mode Control of Micro Gyroscope

2021

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In this paper, an adaptive double feedback fuzzy neural fractional order sliding control approach is presented to solve the problem that lumped parameter uncertainties cannot be measured and the parameters are unknown in a micro gyroscope system. Firstly, a fractional order sliding surface is designed, and the fractional order terms can provide additional freedom and improve the control accuracy. Then, the upper bound of lumped nonlinearities is estimated online using a double feedback fuzzy neural network. Accordingly, the gain of switching law is replaced by the estimated value. Meanwhile, the parameters of the double feedback fuzzy network, including base widths, centers, output layer weights, inner gains, and outer gains, can be adjusted in real time in order to improve the stability and identification efficiency. Finally, the simulation results display the performance of the proposed approach in terms of convergence speed and track speed.

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Novel Neural Network Fractional-Order Sliding-Mode Control With Application to Active Power Filter

Cited by 142 publications

References 36 publications

Synchronization of Active Power Filter under Distorted Grid Conditions

Synchronization of Active Power Filter under Distorted Grid Conditions

Robust Observer Based on Fixed-Time Sliding Mode Control of Position/Velocity for a T-S Fuzzy MEMS Gyroscope

Multiple Loop Fuzzy Neural Network Fractional Order Sliding Mode Control of Micro Gyroscope

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