Open AccessModified fuzzy neural network control using sliding mode technique for power quality improvement system with experimental verification
Author(s) -
Hou Shixi,
Chu Yundi,
Fei Juntao
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2020.0770
Subject(s) - control theory (sociology) , artificial neural network , computer science , stability (learning theory) , sliding mode control , fuzzy logic , fuzzy control system , filter (signal processing) , lyapunov stability , mode (computer interface) , power (physics) , control engineering , control (management) , engineering , artificial intelligence , nonlinear system , machine learning , physics , quantum mechanics , computer vision , operating system
This study mainly focuses on the development of a second‐order sliding mode‐based meta‐cognitive fuzzy neural network (MCFNN) control for power quality improvement, whereby an MCFNN is employed to learn modelling uncertainties. First, a second‐order sliding mode control (SOSMC) is designed to track reference current for active power filter. Then, an MCFNN system with accurate approximation capability is further investigated to learn the unknown parts in SOSMC. Different from the existing predefined structure approaches, only necessary data can be extracted to adjust the structure and parameters of the networks in MCFNN. Subsequently, the Lyapunov stability analysis is presented to guarantee tracking performance and stability of the closed‐loop system. Moreover, the excellent performance of the proposed MCFNN scheme is verified by simulation and experimental studies, and its remarkable characteristics are exhibited in comparison with other intelligent control schemes.