Identification of Permanent Magnet Synchronous Motor Model Based on Theoretical Model and Proxy Model
Abstract: This article mainly designs and analyzes the parameter identification technology methods for permanent magnet synchronous motors based on theoretical models and surrogate models. Firstly, the importance of parameter identification for permanent magnet synchronous motors was introduced, as well as the current main parameter identification methods and possible problems. Then, a mathematical model of the three-phase permanent magnet synchronous motor was provided for the design and analysis of subsequent parameter identification algorithms; Next, the main parameter identification method, the least squares method, was introduced, and the principle, assumptions, and geometric significance of the least squares method were described. The mathematical expressions of batch processing least squares method, recursive least squares method, forgetting factor recursive least squares method, and multi information least squares method were derived in detail; Secondly, this article combines radial basis function neural networks to design a parameter identification method based on surrogate models, and provides detailed mathematical expressions; Finally, using PMSM simulation data under PID control, ESO based speed and current loop control (C-ESO), and ESO based composite current loop control (COM-ESO), the identification effects of forgetting factor recursive least squares (RLS), multi information recursive least squares (MILS), and RBF proxy model-based parameter identification method (RBF-RLS) were compared and analyzed.
Submission Number: 200
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