Maximum Power Extraction from a PMSG-Based Standalone WECS via Neuro-Adaptive Fuzzy Fractional Order Super-Twisting Sliding Mode Control Approach with High Gain Differentiator

Maximum Power Point Tracking (MPPT) in permanent-magnet synchronous generator (PMSG)-based wind energy conversion systems (WECS) remains challenging owing to strong nonlinearities, parametric uncertainties, and external disturbances. Conventional sliding mode control (SMC) strategies, while robust, suffer from chattering, dependence on full-state measurements, and degraded performance under model mismatch, limiting their practical deployment. To address these issues, this study proposes a neuroadaptive fuzzy fractional-order super-twisting sliding mode control (Fuzzy-FOSTSMC) integrated with a high-gain observer (HGO) and a radial basis function neural network (RBFNN). The HGO estimates unmeasurable higher-order states (e.g., angular acceleration), enabling output-feedback implementation. In contrast, the RBFNN online approximates unknown nonlinear system dynamics Lf2h(x) and LgLfh(x), rendering the controller model-free. Chattering is eliminated by replacing the discontinuous signum function with an adaptive fuzzy boundary layer that dynamically modulates the slope near the sliding surface. Stability is theoretically confirmed by Lyapunov analysis. Extensive MATLAB/Simulink simulations verify that the proposed approach yields a tracking precision of 99.96%, a steady-state speed error of 0.018 rad/s, and a 58.2% reduction in the integral absolute error (IAE) compared to the traditional FOSTSMC. It achieves the optimal power coefficient (Cp=0.4762) via TSR control at 7.000±0.002, under ±30% parametric uncertainties, demonstrating excellent robustness and MPPT effectiveness.