Parallel Predictive Torque Control for Induction Machines without Weighting Factors

Fengxiang Wang, Haotian Xie, Qing Chen, S. Alireza Davari, Jose Rodriguez, Ralph Kennel

Resultado de la investigación: Contribución a una revistaArtículo

5 Citas (Scopus)


Finite control set model predictive control (FCS-MPC) calculates torque and flux tracking errors via a cost function that is used for selecting the optimal vector. Compared with field oriented control, FCS-MPC has the merit of a faster dynamic performance because it eliminates both pulsewidth modulation and inner proportion-integration controllers. However, the weighting factor for modifying torque and flux terms must be tuned in accordance with varying operating conditions; this is an area in which further research is needed. In this paper, a parallel predictive torque control (PPTC) with predefined constraints is proposed as a solution for this problem. The PPTC method optimizes torque and flux terms simultaneously, and switching-state candidates are then selected in an adaptive mechanism. The key feature is that torque and flux tracking errors are constrained within the initial boundaries. The proposed PPTC is compared with the state-of-the-art predictive torque control (PTC) method. Both simulation and experimental results confirm that the proposed method, which has no weighting factor, achieves an even better dynamic performance and robustness than the conventional PTC.

Idioma originalInglés
Número de artículo8735918
Páginas (desde-hasta)1779-1788
Número de páginas10
PublicaciónIEEE Transactions on Power Electronics
EstadoPublicada - feb 2020

Áreas temáticas de ASJC Scopus

  • Ingeniería eléctrica y electrónica

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