[Objective] To address the strong parameter dependence of permanent magnet synchronous motor (PMSM) model predictive current control (MPCC), this paper proposes a near-current-variation-based prediction model, which eliminates the resistance and rotor flux parameters, thereby improving the parameter robustness of PMSM MPCC. [Methods] Based on the traditional current prediction model of PMSM and near-current-variation, the predicted current value for applying the same voltage vector Vn as the previous moment was first calculated. Then, using this as a reference value, the predicted values for applying other voltage vectors were derived, thereby establishing a near-current-variation-based prediction model. Finally, through simulations and experiments, the performance of PMSM MPCC based on the traditional current prediction model, incremental current prediction model, and near-current-variation prediction model was compared. [Results] The simulation and experimental results demonstrated that the proposed near-current-variation prediction model did not require stator resistance or rotor flux linkage parameters, and its control performance was comparable to that of both the traditional and incremental current prediction models. Moreover, the optimal voltage vector selections of the three models were consistent. The proposed model relied solely on the stator d, q-axis inductance, significantly reducing parameter dependency while maintaining stable system operation. [Conclusion] The proposed model provides a more simplified solution for PMSM MPCC.