Type-2 Neuro-Fuzzy Control for a Class of Nonlinear Systems

被引：0
|
作者：
Hou, Shixi [1 ]
Wang, Cheng [1 ]
Zhai, Suwei [2 ]
Chu, Yundi [1 ]
机构：
[1] Hohai Univ, Jiangsu Key Lab Power Transmiss & Distribut Equip, Nanjing 210098, Peoples R China
[2] Elect Power Res Inst Yunnan Power Grid Co Ltd, Kunming 650500, Yunnan, Peoples R China
来源：
PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年
基金：
中国国家自然科学基金;
关键词：
Nonlinear systems; terminal sliding mode control; fuzzy neural network; active power filter; ADAPTIVE-CONTROL;
D O I：
10.1109/CCDC52312.2021.9602636
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a type-2 neuro-fuzzy control for a class of nonlinear systems is studied. Firstly, an integral-type sliding mode controller (SMC) is designed to ensure that the error converges in a finite time. At the same time, the saturation function as an effective way to alleviate chattering is utilized. Moreover, a type-2 neuro-fuzzy networks (T2NFN), in which network parameters can be adjusted online, is used to approximate the designed SMC. In order to improve the generalization ability, T2NFN combines a recursive feature selection algorithm. In particular, due to the added robust compensator, the issue of the approximation error also can be overcome. Finally, the T2NFN controller is applied to the active power filter (APF) to show its superiority.
引用
收藏
页码：1343 / 1347
页数：5
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