Neural Network Complementary Sliding Mode Current Control of Active Power Filter

被引：13

作者：

Fei, Juntao ^{[1
,2
,3
]}

Liu, Nixuan ^{[2
]}

Hou, Shixi ^{[2
,3
]}

Fang, Yunmei ^{[1
]}

机构：

[1] Hohai Univ, Coll Mech & Elect Engn, Changzhou 213022, Jiangsu, Peoples R China

[2] Hohai Univ, Coll IoT Engn, Changzhou 213022, Jiangsu, Peoples R China

[3] Jiangsu Key Lab Power Transmiss & Distribut Equip, Changzhou 213022, Jiangsu, Peoples R China

来源：

IEEE ACCESS | 2021年 / 9卷

基金：

美国国家科学基金会;

关键词：

Active filters; Power harmonic filters; Neural networks; Artificial neural networks; Harmonic analysis; Backstepping; Switches; RBF neural network; complementary sliding mode; backstepping control;

D O I：

10.1109/ACCESS.2021.3056224

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This study proposed an adaptive Radical Basis Function (RBF) neural control strategy with a complementary sliding mode approach to compensate the harmonic current in an Active Power Filter (APF). A backstepping algorithm is incorporated to simplify the design procedure. Meanwhile, a complementary sliding surface is employed to replace the standard sliding surface to eliminate the chattering. A neural estimator is designed to approximate the upperbound of the lumped nonlinearities in the APF. A simulation and real-time prototype using TMS320F28335 was built to demonstrate the validity of the proposed controller.

引用

页码：25681 / 25690

页数：10

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