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<p>This paper proposes a novel finite-time adaptive fractional-order sliding mode control strategy to maximize energy conversion efficiency in doubly fed induction generator–based wind turbine (DFIG-WT) systems. A nonsingular fractional-order sliding surface is first constructed to ensure practical finite-time convergence of the system states. An adaptive radial basis function neural network is then employed to estimate lumped uncertainties, enabling model-independent robustness against parameter variations and external disturbances. Moreover, a continuous control structure that combines fractional-order dynamics, neural compensation, and a hyperbolic tangent approximation is designed to suppress chattering and produce smooth control signals. Stability of the closed-loop system is rigorously established using fractional-order Lyapunov theory. Extensive numerical simulations rigorously validate the effectiveness of the proposed controller, demonstrating superior tracking accuracy and faster dynamic response, as quantified by performance metrics such as the root-mean-square error (RMSE), maximum absolute error (MAE), and response time, in comparison with a conventional sliding mode control scheme.</p>

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Finite-time chatter-free control for wind turbines: an adaptive fractional-order sliding mode strategy with neural network compensation

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Abdesselem Boulkroune,
  • Seife-ddine. Boudjemia,
  • Naamane Bounar

摘要

This paper proposes a novel finite-time adaptive fractional-order sliding mode control strategy to maximize energy conversion efficiency in doubly fed induction generator–based wind turbine (DFIG-WT) systems. A nonsingular fractional-order sliding surface is first constructed to ensure practical finite-time convergence of the system states. An adaptive radial basis function neural network is then employed to estimate lumped uncertainties, enabling model-independent robustness against parameter variations and external disturbances. Moreover, a continuous control structure that combines fractional-order dynamics, neural compensation, and a hyperbolic tangent approximation is designed to suppress chattering and produce smooth control signals. Stability of the closed-loop system is rigorously established using fractional-order Lyapunov theory. Extensive numerical simulations rigorously validate the effectiveness of the proposed controller, demonstrating superior tracking accuracy and faster dynamic response, as quantified by performance metrics such as the root-mean-square error (RMSE), maximum absolute error (MAE), and response time, in comparison with a conventional sliding mode control scheme.