Akademik Veri Yönetim Sistemi
VII. International Turkic World Congress on Science and Engineering , Priştine, Kosova, 13 - 15 Kasım 2025, ss.845-855, (Tam Metin Bildiri)
This study presents a high-performance parameter estimation framework for Model Predictive Current Control-based Permanent Magnet Synchronous Motors (PMSMs) drives by integrating an Extended Kalman Filter (EKF) within a MATLAB/Simulink environment. The method simultaneously estimates five critical quantities: stator current components in the stationary reference frame, rotor mechanical speed, load torque, and stator resistance. Accurate estimation of these states and parameters is essential for achieving high-performance sensorless control, fault detection, and real-time performance optimization in drive systems. The EKF is designed based on the nonlinear dynamic model of the PMSM, explicitly accounting for the coupling between electrical and mechanical dynamics. Simulation test scenario includes step-like changes both in load torque and stator resistance to assess robustness. Results demonstrate that rotor speed can be reliably reconstructed despite significant parameter changes, maintaining minimal steady-state error and rapid convergence. The control system effectively tracks both transient and steady-state behaviors of the PMSM without requiring additional mechanical sensors, reducing cost and complexity. The findings confirm that the EKF-based approach provides a high-performance and practical solution for PMSM parameter estimation and control enhancement.