Aiming at the problem of model mismatch caused by time-varying parameters of permanent magnet synchronous motor (PMSM) in wind turbine pitch system in complex operating environment, a model-free predictive current control (HBF-MFPCC) scheme for PMSMs, integrating ultra-local modeling, an HBF neural network observer, and an improved dual-vector modulation strategy, is proposed. A first-order ultra-local model is employed to construct the predictive model for the proposed model-free current control, enabling future current prediction based solely on historical current and voltage data.. The current value in the future can be predicted only by using the historical information such as current and voltage of the motor, and the dependence on the parameters such as resistance, inductance and flux linkage of the motor is eliminating the dependence entirely, which solves the problem that the traditional model predictive current control (MPCC) depends on accurate motor parameters. A HBF neural network observer is designed to quickly identify the lumped error of the prediction model. The decision tree is used to optimize the center and width of the basis function. The observer has high identification speed and adaptability, which significantly enhances the accuracy of the prediction model. An improved dual-vector optimal duty cycle modulation strategy is adopted. The optimal vector is selected from 19 possible voltage vector combinations to drive the inverter. Adaptive time allocation is then applied to suppress current ripple, thereby improving current tracking performance. The simulation and experimental results show that the proposed HBF-MFPCC strategy can reduce the current tracking error by 50 % and the harmonic distortion rate by 28 % compared with the MPCC strategy under the condition of simulating extreme parameter mismatch. The designed HBF neural network observer can reduce the current tracking error by 53 % and the harmonic distortion rate by 55 %. The improved double vector modulation method can reduce the current tracking error by 24 % and the harmonic distortion rate by 11 %. This scheme can significantly improve the robustness of the system and ensure good current tracking performance.