Parameter Estimation of SPMSM Using Adaptive Filter and Extended Sliding-Mode Observer

Abstract

We propose a parameter estimation algorithm for surface-mounted permanent magnet synchronous motors (SPMSMs) using normalized least mean square (NLMS) adaptive filter and extended sliding-mode mechanical parameter observer (ESMMPO). The adaptive filter estimates electrical parameters, stator inductance, resistance, and rotor linkage flux. The ESMMPO estimates the system disturbance, from which mechanical parameters are extracted. The key feature of the proposed algorithm is that the two estimation algorithms for electrical and mechanical parameters are effectively integrated into a single framework, and the estimated rotor linkage flux is used for the system disturbance estimation of the ESMMPO in real time, which results in the accuracy improvement of the mechanical parameters. Performing extensive simulation experiments for the proposed algorithm, we verify the system parameter estimation performance for SPMSMs.