A modified RBF neuro-sliding mode control technique for a grid connected PMSG based variable speed wind energy conversion system

DOI10.1155/2018/1780634zbMath1403.93063OpenAlexW2895536802WikidataQ129166803 ScholiaQ129166803MaRDI QIDQ1629470

François Béceau Pelap, Armel Simo Fotso, Rostand Marc Douanla, Godpromesse Kenné

Publication date: 12 December 2018

Published in: Journal of Control Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2018/1780634

zbMATH Keywords

neural networkneuro-sliding mode controlwind energy conversion

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Nonlinear systems in control theory (93C10) Application models in control theory (93C95) Variable structure systems (93B12)

Uses Software

• Simulink

Cites Work

• Maximum wind power tracking of doubly fed wind turbine system based on adaptive gain second-order sliding mode
• Rotor speed control of a direct-driven permanent magnet synchronous generator-based wind turbine using phase-lag compensators to optimize wind power extraction
• Radial Basis Function (RBF) Neural Network Control for Mechanical Systems
• Maximum power extraction on wind turbine systems using block‐backstepping with gradient dynamics control
• A new adaptive control strategy for a class of nonlinear system using RBF neuro-sliding-mode technique: application to SEIG wind turbine control system
• Unnamed Item

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