This study introduces a novel fractional-order sliding mode control (FOSMC) strategy for DFIG-based wind turbines, aiming to optimize energy conversion efficiency. The approach combines FOSMC with radial basis function neural networks (RBFNNs) to achieve a fast practical finite-time convergence and eliminate chattering through a specially designed nonsingular sliding surface. System robustness is reinforced by a RBFNN-driven real-time uncertainty estimation. Simulation results confirm the controller’s high tracking precision, strong robustness against uncertainties, and complete suppression of chattering effects.
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S. Boudjemia
Abdesselem Boulkroune
N. Bounar
IFAC-PapersOnLine
University of Jijel
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Boudjemia et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69a75f98c6e9836116a2b124 — DOI: https://doi.org/10.1016/j.ifacol.2026.01.035