Abstract: Traditional finite control set model predictive current control (MPCC) for permanent magnet synchronous motor drives suffers from high current ripple and the system parameter dependence problem. To address these issues, a two-vector-based MPCC with online parameter identification is proposed. First, to eliminate the computational burden caused by the diversified combination of voltage vectors, according to the real-time control performance of increasing or decreasing the q-axis current, a two-vector combination principle that constrains the optional range of the first and second voltage vectors is designed. Subsequently, to reduce the current ripple in the whole control cycle, a cost function which contains the current tracking error term at the switching point of the two vector is designed. The cost function is used to evaluate candidate vector combinations and select the optimal one. Besides, to improve the parameter robustness of the system, a cascade model reference adaptive system-based multi- parameter identification method is proposed. Based on establishing the d- and q-axis incremental current state equation and integrating with q-axis current state equation, the full rank state equation can be obtained, thus the identification of the stator resistance, inductance and flux can be realized. Finally, the effectiveness of the proposed strategy is verified by experiments.