Conventional Model Predictive Current Control (MPCC) for Permanent Magnet Synchronous Motor (PMSM) relies heavily on accurate mathematical models and motor parameters,and its ability to handle model mismatches and parameter variations is limited.Hence,this paper proposes a Dual-Vector Model-Free Predictive Current Control (DV-MFPCC) strategy based on an Adaptive Strong Tracking Extended Kalman Observer (ASTEKO).First,the parameter-dependent and unknown components of the original mathematical model are unified as lumped disturbances,and an extended ultra local model of PMSM is established.Second,the ASTEKO is designed to estimate the nonlinear parts of the model,and a sliding window is employed to adjust the residual threshold,thereby improving the sensitivity to state variations.Finally,by integrating dual-vector MFPCC,the current prediction equation is constructed,and voltage vectors are output to minimize the error between the dq-axis currents and their reference values.Simulation results show that the proposed strategy outperforms both single-vector MFPCC and dual-vector MPCC.Experimental results validate the effectiveness of the proposed control strategy,demonstrating precise current control,rapid dynamic response,and strong robustness.