Transactions on Machine Intelligence 2821-1693 3 1 2020 03 01 Estimation of Angular Velocity and Position of a Permanent Magnet Synchronous Motor Using Discrete-Time Extended Kalman Filter and Hybrid Extended Kalman Filter 23 33 159757 10.47176/TMI.2020.23 EN S. Talaee Malek Ashtar University of Technology (MUT), Tehran, Iran S. Nasrollahi Malek Ashtar University of Technology (MUT), Tehran, Iran M. R. Arvan Sharif University of Technology, Tehran, Iran Journal Article 2019 12 25 This paper presents an analytical study of the Permanent Magnet Synchronous Motor (PMSM), focusing on the estimation of its rotor's angular position and velocity using nonlinear filtering techniques. Due to the inherent nonlinearity of the PMSM's state-space equations, conventional linear estimation methods are insufficient for accurate state estimation. In this work, the motor's armature current being a directly measurable quantity is utilized as the primary observable to estimate the system’s internal states, specifically the rotor’s angular position and angular velocity. Two advanced estimation algorithms are employed and compared: the Discrete Extended Kalman Filter (DEKF) and the Hybrid Extended Kalman Filter (HEKF). These algorithms are adapted to handle the nonlinear nature of the PMSM dynamics and to operate effectively within a discrete-time framework. Simulation results reveal that both filters are capable of estimating rotor dynamics with reasonable accuracy. However, the hybrid Kalman filter demonstrates superior performance in terms of estimation precision and convergence speed. The findings highlight the effectiveness of hybrid nonlinear filtering approaches in improving the observability and control performance of PMSM systems, particularly in sensorless or low-cost applications where direct measurement of rotor position is impractical. https://www.tmachineintelligence.ir/article_159757_99790e3ad90157968691bc69b0970056.pdf