A Self-Organizing Global Sliding Mode Control and Its Application to Active Power Filter

被引：57
作者：
Hou, Shixi [1 ,2 ]
Fei, Juntao [1 ,2 ]
机构：
[1] Hohai Univ, Jiangsu Key Lab Power Transmiss & Distribut Equip, Nanjing 210098, Peoples R China
[2] Hohai Univ, Coll IOT Engn, Nanjing 210098, Peoples R China
来源：
IEEE TRANSACTIONS ON POWER ELECTRONICS | 2020年 / 35卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Active power filter (APF); adaptive control; fuzzy neural network (FNN); sliding mode control (SMC); FUZZY-NEURAL-NETWORK; BACKSTEPPING CONTROL; MOTION CONTROL; OBSERVER; SYSTEMS; DESIGN; DRIVE;
D O I：
10.1109/TPEL.2019.2958051
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this article, a self-organizing global sliding mode control (GSMC) is developed for a class of dynamic systems, whereby modeling uncertainties are estimated by metacognitive fuzzy-neural-network (MCFNN) framework. First, a GSMC is designed for the tracking of reference signals to eliminate the reaching mode and chattering phenomenon. To overcome the drawbacks of GSMC, the control law is designed based on MCFNN instead of the uncertain information. Distinguished from the fixed structure schemes, MCFNN can restructure the network structure and parameters by extracting useful input data not all data. Moreover, in order to alleviate redundant or inefficient computation, only the parameters of the rule nearest to the current data instead of all rules are updated online based on Lyapunov stability analysis. Finally, simulation and experimental investigations on active power filter are employed to verify the control performance of proposed controller.
引用
收藏
页码：7640 / 7652
页数：13
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[1]   INSTANTANEOUS REACTIVE POWER COMPENSATORS COMPRISING SWITCHING DEVICES WITHOUT ENERGY-STORAGE COMPONENTS [J].
AKAGI, H ;
KANAZAWA, Y ;
NABAE, A .
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 1984, 20 (03) :625-630
[2]   Neural-Network-Based Adaptive Backstepping Control With Application to Spacecraft Attitude Regulation [J].
Cao, Xibin ;
Shi, Peng ;
Li, Zhuoshi ;
Liu, Ming .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (09) :4303-4313
[3]   Adaptive Neural Fault-Tolerant Control of a 3-DOF Model Helicopter System [J].
Chen, Mou ;
Shi, Peng ;
Lim, Cheng-Chew .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 46 (02) :260-270
[4]   Adaptive Global Sliding-Mode Control for Dynamic Systems Using Double Hidden Layer Recurrent Neural Network Structure [J].
Chu, Yundi ;
Fei, Juntao ;
Hou, Shixi .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 31 (04) :1297-1309
[5]   Dynamic global proportional integral derivative sliding mode control using radial basis function neural compensator for three-phase active power filter [J].
Chu, Yundi ;
Fei, Juntao ;
Hou, Shixi .
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2018, 40 (12) :3549-3559
[6]   Global sliding-mode observer with adjusted gains for locally Lipschitz systems [J].
Efimov, D. ;
Fridman, L. .
AUTOMATICA, 2011, 47 (03) :565-570
[7]   Self-Organizing Recurrent Fuzzy Wavelet Neural Network-Based Mixed H2/H∞ Adaptive Tracking Control for Uncertain Two-Axis Motion Control System [J].
El-Sousy, Fayez F. M. ;
Abuhasel, Khaled Ali .
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2016, 52 (06) :5139-5155
[8]   Double Hidden Layer Output Feedback Neural Adaptive Global Sliding Mode Control of Active Power Filter [J].
Fei, Juntao ;
Chu, Yundi .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2020, 35 (03) :3069-3084
[9]   A Self-Organizing Sliding-Mode Controller for Wastewater Treatment Processes [J].
Han, Honggui ;
Wu, Xiaolong ;
Qiao, Junfei .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2019, 27 (04) :1480-1491
[10]   Finite-Time Adaptive Fuzzy-Neural-Network Control of Active Power Filter [J].
Ho, Shixi ;
Fei, Juntao ;
Chen, Chen ;
Chu, Yundi .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2019, 34 (10) :10298-10313
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