Modified fuzzy neural network control using sliding mode technique for power quality improvement system with experimental verification | Zendy

Hou Shixi | Zendy; Chu Yundi | Zendy; Fei Juntao | Zendy

Open Access

Modified 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 , power quality , computer science , fuzzy logic , control engineering , control (management) , sliding mode control , power (physics) , mode (computer interface) , fuzzy control system , artificial intelligence , engineering , nonlinear system , physics , quantum mechanics , 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.

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