Parallel Predictive Torque Control for Induction Machines without Weighting Factors

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(FOC), FCS-MPC has the merit of a faster dynamic performance because it eliminates both pulse-width modulation (PWM) and inner proportion-integration (PI) 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 adaptive 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 optimized boundaries. The proposed PPTC is compared with the state-of-the-art 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.     «

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(FOC), FCS-MPC has the merit of a faster dynamic performance because it eliminates both pulse-width modulation (PWM) and inner proportion-integration (PI) controllers. However, the weighting factor for modifying torque and flux terms must be tuned in accordance with varying operating conditions; th...     »