K-best-Sphere-Decoding-Based Model Predictive Control for Dual Three-Phase SPMSMs

The long prediction horizon shows remarkable advantages of steady-state operation and current distortions for the finite control set model predictive control (FCS-MPC) using power conversion devices. However, the computation is inevitably increased exponentially, especially for the multi-phase motors. In this article, K-best-sphere decoding algorithm (kSDA) is used to reduce the computational burden by transforming the optimization problem into an integer least-squares (ILS) problem. The kSDA-based MPC with breadth-first strategy is applied to the dual three-phase surface-mounted permanent magnet synchronous machine (DTP-SPMSM) drive system. The experimental verification is carried out to validate the feasibility of the proposed kSDA-based MPC, which well agrees with the theoretical analysis. In particular, kSDA is a more efficient algorithm to solve long prediction horizon problem by comparing with SDA.