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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Vydané v:IEEE transactions on power electronics Ročník 35; číslo 9; s. 9904 - 9922
Hlavní autori: Fei, Juntao, Chen, Yun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0885-8993
1941-0107
DOI:10.1109/TPEL.2020.2974470