[Objective] Aiming at the nonlinear distortion of d-q axis inductance caused by magnetic saturation effects in the operation of synchronous reluctance motor (SynRM) drive systems, this paper proposes an online inductance parameter identification strategy based on robust recursive least square (RRLS). [Methods] Firstly, the predicted voltage difference was calculated to construct a historical prediction residual sequence, and rolling optimization was performed during motor operation to effectively reduce steady-state estimation errors caused by random data. Secondly, the predicted standard deviation was used as a robust scale to construct a robust loss function, which enhanced the algorithm’s ability to resist load disturbances without significantly increasing the computational burden. Then, an approximate equilibrium condition was combined with an adaptive mechanism with a variable forgetting factor for recursive estimation, and accurate parameter values were obtained through multiple iterations. Finally, a SynRM control and parameter identification system was built in Matlab/Simulink, and the RRLS algorithm was compared with the traditional variable forgetting factor recursive least square (VFFRLS) under different operating conditions. [Results] The simulation results showed that under no-load and load disturbance conditions, the proposed RRLS algorithm had lower identification errors. The steady-state error of the d-axis inductance was less than 0.5%, and the steady-state error of the q-axis inductance was less than 4%. During the dynamic process, the d-axis overshoot was reduced from 25 mH by the VFFRLS algorithm to 12 mH by the proposed RRLS algorithm, and the q-axis overshoot was reduced from 33 mH to 13 mH. [Conclusion] Compared with the traditional VFFRLS algorithm, the RRLS algorithm proposed in this paper achieves high steady-state identification accuracy, reduces overshoot during dynamic processes, and demonstrates excellent online identification performance under load disturbances, with high system robustness.