Low-Computation Burden Model Predictive Current Control for PMSM Drives Based on Cartesian Distance Minimization

Model predictive current control (MPCC) has attracted a lot of attention due to its many merits, such as fast-dynamic response and simplicity. However, the large computation burden and high dependence on machine parameters represent problems for applying MPCC in PMSM applications. This paper proposes a low-computation-burden MPCC based on Cartesian distance minimization principle in choosing the optimal voltage vector. In order to reduce dependence on machine parameters, the conventional voltage vector hexagon is expanded to a larger hexagon consists of 19 virtual voltage vectors. The ideal voltage vector in next control period is derived from the reference current. The optimal voltage vector is then chosen from only 2 voltage vectors, thus decreasing the computation burden. The effectiveness of the proposed method is verified by simulation results.