Parameter Identification for SPMSM Based on a Superior ROA

To enhance the accuracy of parameter identification in surface-mounted permanent magnet synchronous motor (SPMSM), this article proposes a superior raccoon optimization algorithm (SROA). First, the article incorporates a migration optimization strategy, an optimal raccoon random interaction strategy, and multidimensional one-way heuristic self-learning tactics with the original raccoon optimization algorithm (ROA). This integration addresses the limitations of the conventional ROA, creating the new SROA. Second, the performance of the SROA is evaluated against other algorithms using a subset of the IEEE CEC2015 test suite functions. Subsequently, the SROA is utilized to solve the SPMSM parameter identification objective function, leading to the precise determination of the motor's stator resistance, inductance, and permanent magnet flux linkage. Finally, the SROA demonstrates significant improvements in convergence speed, solution accuracy, and solution stability. Experimental validation is conducted using a motor control platform, with the algorithm implemented on a motor control chip. The results show that the recognition performance of SROA is consistent with the simulation outcomes. This article is accompanied by a demo video, raw data file, and the code for the SROA.