A Universal Multiple-Vector-Based Model Predictive Control of Induction Motor Drives

Conventional finite control set-model predictive control (FCS-MPC) applies single voltage vector within each control period. This leads to relatively high steady-state ripples and requires fast sampling rate. Additionally, enumeration-based optimal vector selection is computationally intensive. Recently, double-vector-based schemes have been proposed to improve the steady-state performance of FCS-MPC. However, they are usually complicated in vector selection and duty ratio calculation. In this paper, a universal multiple-vector-based MPC (UMV-MPC) is proposed, which achieves the same performance as the state-of-the-art double-vector-based MPC, but executes in a much more efficient and universal way. Unlike conventional FCS-MPC, enumerating process and state predictions for candidate voltage vectors are not required in the proposed UMV-MPC to select the best voltage vectors. In UMV-MPC, the optimal vectors and duty ratios are directly constructed from deadbeat control (DBC) based on space vector modulation (SVM), which is easy to follow and quick to use. The proposed UMV-MPC is not only more efficient than prior methods, but also reveals the inherent relationship between double-vector-based MPC and DBC with SVM. A comparative study of UMV-MPC and prior double-vector-based MPC is carried out in this paper. The theoretical analysis as well as simulation and experimental tests on a 2.2-kW induction motor drive are demonstrated to validate the effectiveness of the proposed UMV-MPC.