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

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Abstract
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. 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 (IM) drive are demonstrated to validate the effectiveness of the proposed UMV-MPC.
Funding Information
  • National Natural Science Foundation of China (51577003)
  • Natural Science Foundation of Beijing Municipality (3162012)

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