Abstract
This paper presents an interesting hybrid solution to a challenging estimation and control problem of the Permanent Magnet Synchronous Motor (PMSM). Apart from the inherently nonlinear nature of the PMSM, which makes this problem particularly challenging, is the unavailability of the measurements, rotor position, and speed. In an effort to efficiently cope with such issues along with the random noise environment, the Unscented Kalman Filter (UKF) is chosen to estimate the states of the PMSM dynamic system and the Model Predictive Control (MPC) is utilized to control the state space vector in Pulse Width Modulation (PWM). Additionally, the MPC has also been implemented in combination with the Extended Kalman Filter (EKF) and also with Sliding Mode Control (SMC), in order to vigorously compare these hybrid approaches in terms of accuracy, robustness, and transient response. The MPC-UKF, a combination that has never been implemented before, outperforms the other two by efficiently dealing with the issues of high nonlinearities, by accurately estimating the states while the measurements were practically unavailable, and coping with the fast dynamics of the PMSM.