A Dual-Vector Model Predictive Current Control Based on Weighting Amplitude Slope with Parameter Identification

Conventional model predictive current control (MPCC) bears high current fluctuations and the system parameter dependence problem. To address these issues, an improved dual-vector MPCC (DMPCC) with parameter identification is proposed in this paper. To reduce the number of alternative active voltage vectors, two adjacent active voltage vectors (AVVs) are first preselected by the reference weighting amplitude slope (WAS) of the stator current, and the action time of the two-vector combinations are calculated by WAS. Then, the cost function is used to determine the optimal dual-vector combination to improve the steady-state performance. Meanwhile, to solve the rank deficiency issue of multiple parameter identification, an improved MRAS parameter identification algorithm based on the incremental state equation is proposed. Finally, the feasibility and effectiveness of the proposed control algorithm are verified by experiments.