Full-Speed Domain Parameter Identification of Surface-Mounted PMSLM Based on Dual Axis Injection

This article proposes an online estimation strategy for reducing parameter identification errors in surface-mounted permanent magnet synchronous linear motor (SPMSLM) at low speeds. The strategy is based on injecting signals into dual axis, enabling full-speed domain parameter identification. First, sensitivity analysis is conducted on resistance, inductance, and flux linkage concerning speed and torque, revealing significant parameter identification errors in traditional voltage model-based methods under low-speed and low-current conditions. Next, the impact of sampling noise on parameter identification is analyzed when the sampling signal is small. Subsequently, an identification strategy is proposed that simultaneously injects signals in both the d-q and
$\alpha $
-
$\beta $
axes to accurately estimate resistance and inductance at low speed and low current. Finally, experiments were conducted on an SPMSLM, and the results indicated that the proposed method significantly reduces identification errors caused by low signal-to-noise ratio during low speeds.