Parameter Estimation of Permanent Magnet Synchronous Motor Using Orthogonal Projection and Recursive Least Squares Combinatorial Algorithm

Abstract

This paper presents parameter estimation of Permanent Magnet Synchronous Motor (PMSM) using a combinatorial algorithm. Nonlinear fourth-order space state model of PMSM is selected. This model is rewritten to the linear regression form without linearization. Noise is imposed to the system in order to provide a real condition, and then combinatorial Orthogonal Projection Algorithm and Recursive Least Squares (OPA&RLS) method is applied in the linear regression form to the system. Results of this method are compared to the Orthogonal Projection Algorithm (OPA) and Recursive Least Squares (RLS) methods to validate the feasibility of the proposed method. Simulation results validate the efficacy of the proposed algorithm.

Application of recent nature‐inspired meta‐heuristic optimisation techniques to small permanent magnet DC motor parameters identification problem

“…Other methods like the algebraic identification method [3, 4] and the recursive least square method [5] have also been used. An expansion of the latter method has also been reported in parameter estimation of a permanent magnet synchronous motor [6]. A method based on block‐pulse function series and neural networks was used to estimate the parameters of the continuous‐time model of a PMDC [7], while adaptive Tabu‐search method was adopted in [8] to identify the parameters of a separately excited DC motor.…”

Research on online parameter identification of interior permanent magnet synchronous motor based on augmented robust forgetting factor recursive least square

“…The offline identification method could provide the accurate results under certain working conditions, but this method cannot be adapted for the whole working conditions, thus the online identification of parameters is needed to improve the system performance. The online identification mainly focuses on the recursive least square (RLS), model reference adaptive method (MRAS), Extended Kalman Filter (EKF), synovial variable structure, fuzzy control, Neural Network (NN), and so on 8‐12 . Liu et al 13 reported that the adaptive law of MRAS based on the Popov super‐stability theory was used to identify the L d , L q , and ψ f , then combined with MTPA to achieve motor control with high‐precision; Khov et al 14 proposed two‐stage RLSs to estimate the four electrical parameters of IPMSM, using the online data and more accurate identification results; EKF is recently an popular online identification for motor parameters with relatively high accuracy.…”