Low-speed Sensorless MTPA Control of Interior Permanent Magnet Synchronous Motor Based on Parameter Self-learning

This paper proposes a low-speed sensorless maximum torque per ampere (MTPA) control method for interior permanent magnet synchronous motor (IPMSM) based on online parameter self-learning. The method injects high-frequency (HF) square wave voltage on the αβ axis to identify the rotor position, rotor flux linkage and inductance parameters. The identification results are applied to MTPA and sensorless control. A digital signal processor, made by Texas Instruments Company type TMS-320F-28034, is used as the computation center to execute the whole control algorithms. The simulation and experiment of 1.5kW IPMSM prove that this method can effectively improve the efficiency of the low-speed sensorless control system of IPMSM.