Torque Ripple Minimization in Predictive Torque Control Method of PMSM Drive Using Adaptive Fuzzy Logic Modulator and EKF Estimator

In this paper, an improved model predictive direct torque control (MPDTC) based on an adaptive fuzzy logic modulator and an extended Kalman filter is introduced to achieve a higher performance of PMSM drive systems. In fact, conventional MPDTC selects only one voltage vector (VV) per control period by minimizing a standard cost function. Consequently, the torque and flux may not be controlled perfectly, and undesirable ripples inevitably take place in these controlled variables. To overcome this problem, this paper proposes an adaptive fuzzy logic-based duty cycle vector modulation, and two VVs instead of a single VV are applied during the whole control cycle to promote the torque control performance and reduce its undulations. Moreover, a design of an extended Kalman estimator for AFLM-MPDTC-PMSM drive systems is proposed. This combination can effectively improve the overall control system performances by avoiding the extra usage of multiple mechanical sensors, rejecting the external perturbations, reducing the harmonics of the stator current and guaranteeing the accurate prediction model. The effectiveness of the proposed AFLM-MPDTC strategy is well tested and compared with a conventional MPDTC method via simulations performed by MATLAB/Simulink software. The obtained results show an important reduction in ripples for both torque and flux, and a considerable reduction in THD.