Adaptive RBF Neural Network Based on SMC for APF control strategy study

被引：0
|
作者：
Zhang, Huiyue [1 ]
Liu, Yunbo [1 ]
Jiang, Zhengrong [1 ]
机构：
[1] North China Univ Technol, Elect Engn Inst, Beijing 100144, Peoples R China
来源：
2017 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION (ICICTA 2017) | 2017年
关键词：
APF; RBFNN; control strategy; SMC;
D O I：
10.1109/ICICTA.2017.82
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Current harmonics are the major concern in modern equipment. In this paper, an adaptive radical basis function neural network (RBFNN) is proposed to deal with dynamic tracking error problems which are the mathematic model uncertain or complex for the three-phase active power filter (APF). APF is necessary to compensate the harmonics exits in the nonlinear load to maintain the supply current stabilization. The adaptive RBFNN systems are employed to approximate the unknown system function in the sliding mode controller. The simulation results of APF demonstrate the outstanding compensation performance and strong robustness.
引用
收藏
页码：340 / 343
页数：4
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