Parameter Identification Method for Permanent Magnet Synchronous Motors Based on Model Reference Adaptive System

Abstract

During the actual operation of a permanent magnet synchronous motor (PMSM), parameter mismatch can lead to degraded control performance. To address this issue, this paper proposes an online parameter identification method based on the Model Reference Adaptive System (MRAS). The method constructs reference models and adjustable models for the d-axis and q-axis currents of the PMSM. To resolve the rank deficiency problem in the voltage equations, a stepwise identification strategy is adopted: adaptive laws based on Popov's hyperstability theory are first designed to identify the stator inductance; subsequently, utilizing the identified inductance value, adaptive laws are further designed to simultaneously identify the stator resistance and permanent magnet flux linkage. A simulation model is built in Simulink for verification. The results demonstrate that the proposed MRAS method effectively achieves online identification of motor parameters, with identification errors for stator inductance, resistance, and permanent magnet flux linkage being approximately 2.98%, 2.60%, and 3.10% respectively, and a convergence time of about 0.15 seconds. The waveforms are stable and the accuracy meets the requirements for practical engineering applications, verifying the correctness and effectiveness of the method.