Dynamic Terminal Sliding-Mode Control for Single-Phase Active Power Filter Using New Feedback Recurrent Neural Network

In this article, an adaptive dynamic terminal sliding-mode controller using a double hidden layer recurrent neural network (DHL-RNN) structure for a single-phase active power filter (APF) is proposed to improve harmonic compensation performance. First, a method combining dynamic sliding mode and ter...

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Vydáno v:	IEEE transactions on power electronics Ročník 35; číslo 9; s. 9904 - 9922
Hlavní autoři:	Fei, Juntao, Chen, Yun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.09.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Active control Active filters Active power filter (APF) Adaptive filters Biological neural networks Compensation Computer simulation Controllers double hidden layer recurrent neural network (DHL-RNN) dynamic terminal sliding-mode control (DTSMC) hardware-in-the-loop Harmonic analysis Neural networks Nonlinear control Nonlinear dynamical systems Nonlinear dynamics Power harmonic filters Recurrent neural networks Sliding mode control
ISSN:	0885-8993, 1941-0107
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Abstract	In this article, an adaptive dynamic terminal sliding-mode controller using a double hidden layer recurrent neural network (DHL-RNN) structure for a single-phase active power filter (APF) is proposed to improve harmonic compensation performance. First, a method combining dynamic sliding mode and terminal sliding mode is proposed to solve the chattering phenomenon in traditional sliding-mode control. Then, since the nonlinear dynamics of APF is difficult to obtain accurately, the DHL-RNN is used to approximate the proposed dynamic terminal sliding-mode controller. Meanwhile, an integral robust switching term is added to eliminate the approximation error of the neural network. Simulation and experimental results proved that the proposed controller has better compensation performance and tracking effect compared with a simple terminal sliding-mode controller.
AbstractList	In this article, an adaptive dynamic terminal sliding-mode controller using a double hidden layer recurrent neural network (DHL-RNN) structure for a single-phase active power filter (APF) is proposed to improve harmonic compensation performance. First, a method combining dynamic sliding mode and terminal sliding mode is proposed to solve the chattering phenomenon in traditional sliding-mode control. Then, since the nonlinear dynamics of APF is difficult to obtain accurately, the DHL-RNN is used to approximate the proposed dynamic terminal sliding-mode controller. Meanwhile, an integral robust switching term is added to eliminate the approximation error of the neural network. Simulation and experimental results proved that the proposed controller has better compensation performance and tracking effect compared with a simple terminal sliding-mode controller.
Author	Chen, Yun Fei, Juntao
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SubjectTerms	Active control Active filters Active power filter (APF) Adaptive filters Biological neural networks Compensation Computer simulation Controllers double hidden layer recurrent neural network (DHL-RNN) dynamic terminal sliding-mode control (DTSMC) hardware-in-the-loop Harmonic analysis Neural networks Nonlinear control Nonlinear dynamical systems Nonlinear dynamics Power harmonic filters Recurrent neural networks Sliding mode control
Title	Dynamic Terminal Sliding-Mode Control for Single-Phase Active Power Filter Using New Feedback Recurrent Neural Network
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