Online Apparent Inductance Identification Based on Adaptive Suppression of AC Signals in Coupled Axes

Parameter identification can accurately obtain motor control parameters to achieve better control performance. However, the coupling terms in the motor voltage equations introduce AC-coupled currents in traditional parameter identification methods based on transient voltage equations, which affects the identification of inductance parameters. To address this issue, this paper proposes an online apparent inductance identification strategy based on adaptive suppression of AC signals in the coupled axis system, resolving the identification errors caused by the coupling terms in the equations. Building on the analysis of the coupling characteristics in the dq-axis voltage equations of permanent magnet synchronous motors (PMSMs) and the structural features of the motor's dual closed-loop control system, a novel identification method is proposed. This method employs the Adaline adaptive algorithm to suppress AC current signals, enabling dual-axis decoupled voltage injection for parameter identification. Finally, experimental validation was conducted on a 2.2 kW motor back-to-back test platform, confirming the effectiveness of the proposed method.