Online multi‐parameter estimation of permanent magnet synchronous machine with step‐pulse injection

Abstract

Online estimation of the machine parameters is essential to fault diagnosis and high‐performance control of permanent magnet synchronous machine (PMSM). The signal injection is an effective way to estimate machine parameters. However, the inductance variation caused by signal injection will result in difficulties for the estimated parameters to converge. At the same time, online parameters estimation brings heavy burden to the operation of control system. To solve these two problems, optimized inductance model is built with the step‐pulse injection to improve the accuracy of estimation. Compared with the existing method, the step‐by‐step parameter estimation method is adopted and orders of feedback matrix are reduced, which improves the computation efficiency. The forgetting factor recursive least square (FFRLS) algorithm is adopted to estimate stator resistance, rotor flux linkage and d‐, q‐axes inductances without acquiring any nominal parameter. The minimum amplitude and appropriate frequency of step‐pulse are determined by the parameter error and convergence analysis. The effectiveness of the proposed method is verified via a 1.5 kW PMSM drive platform.

Research on Dynamic Identification of Servo Motor Load Inertia Based on the Error Gain Factor Model

“…However, their performance is affected because it is difficult to quickly obtain the moment of inertia of PMSMs, and the control accuracy is reduced [4,5]. Therefore, the dynamic identification of the moment of inertia of PMSMs has very important research significance [6,7].…”