Novel Method to Identify PMSM Parameters Based on Multiple Linear Regressive Models

Abstract

Abstract: A New Coupled Recursive Least Squares (C-FF-RLS) Algorithm with a forgetting factor was proposed for the Parameters Identification of Permanent Magnet Synchronous Motors (PMSMs). The deduced multiple linear regressive models of PMSM were proposed that were simple and appropriate for parameter identification. The C-FF-RLS identification algorithm had a high computational efficiency and a fast convergence speed because which Avoided the Matrix Inversion Operation in the Gain Matrix Compared with the Traditional Multivariable Recursive Least Squares (M-FF-RLS) Algorithm with a Forgetting Factor. The Proposed Identification Algorithm was applied on a simulation system of PMSM. The identification results achieved by the C-FF-RLS Algorithm comparing with the PMSM parameters obtained by the M-FF-RLS Algorithm. The comparisons show that the C-FF-RLS Algorithm performs better than the M-FF-RLS Algorithm for the parameters identification of PMSM.

Key words: PMSM, multiple models, simulation, parameter identification, RLS

CLC Number:

TM301.6

Shi Zhenwei, Ji Zhicheng. Novel Method to Identify PMSM Parameters Based on Multiple Linear Regressive Models[J]. Journal of System Simulation, 2015, 27(8): 1687-1696.

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