Speed Sensorless Control in Permanent Magnet Synchronous Motor DTC Systems

The effectiveness of speed sensorless control in Permanent Magnet Synchronous Motor (PMSM) Direct Torque Control (DTC) systems requires further investigation. In sensorless DTC implementations, motor speed is typically estimated using mathematical observers or algorithms rather than measured directly by physical sensors. Common approaches include model reference adaptive systems (MRAS), sliding mode observers, or extended Kalman filters, which calculate rotor position and speed through voltage and current measurements.

These estimation methods can introduce errors, particularly during dynamic load changes or low-speed operations where back-EMF signals become weak. The implementation often requires precise motor parameters and sophisticated filtering techniques to maintain accuracy. Code implementation typically involves real-time calculation of flux linkages and torque using Clarke/Park transformations, followed by speed estimation algorithms that compare predicted versus actual electrical parameters.

To enhance sensorless performance, researchers often combine multiple estimation techniques or integrate additional sensor data through sensor fusion algorithms. Optimization methods like adaptive filtering, parameter identification routines, or AI-based observers can improve robustness. The reliability assessment must consider algorithm stability across operating conditions, while cost-benefit analysis weighs reduced hardware expenses against potential performance trade-offs in practical applications.