Adaptive Neural Control of Active Power Filter Using Fuzzy Sliding Mode Controller

被引：17

作者：

Wang, Tengteng ^{[1
]}

Fei, Juntao ^{[1
]}

机构：

[1] Hohai Univ, Coll IOT Engn, Changzhou 213022, Peoples R China

来源：

IEEE ACCESS | 2016年 / 4卷

基金：

美国国家科学基金会;

关键词：

Sliding mode control; radial basis function neural network (RBF NN); adaptive fuzzy control; NONLINEAR-SYSTEMS; TRACKING CONTROL; DEAD-ZONE; NETWORK;

D O I：

10.1109/ACCESS.2016.2591978

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 paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance the performance of shunt active power filter (APF). The RBF NN is utilized on the approximation of nonlinear function in the APF dynamic model and the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method connfirm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.

引用

页码：6816 / 6822

页数：7

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