An Efficient Model Predictive Torque Control for Induction Motors With Flexible Duty Ratio Optimization
Xu Wu, Wenxin Huang, Yajun Zhao, Can Huang
Abstract
Efforts to improve the drive performance of classical model predictive torque control (MPTC) for induction motors (IM) have continued for decades. Existing methods often suffer from intensive computations, poor robustness, and limited quality. To resolve these problems, this article proposes an efficient MPTC for IM with flexible duty ratio optimization. In the proposed strategy, candidate voltage vectors are restricted to be in a subset of an area enclosed by voltage limit hexagon, where all the possible two-vector combinations and part of three-vector combinations are included. To quickly determine the optimal input voltage, a tailored duty ratio optimization algorithm based on voltage error minimization is designed. Experiments clearly indicate that the proposed strategy can effectively improve the steady performance with respect to classic MPTC and some other typical methods. More importantly, the proposed strategy can be conveniently extended by increasing the number of three-vector combinations to achieve steady performance close to space vector modulation-based direct torque control (SVM-DTC). It is inspiring to see faster dynamic response together with less torque ripple than SVM-DTC is provided during a large transient due to the rigorously enhanced optimality of input voltage. Finally, the robustness of the proposed scheme is experimentally validated.