Adaptive quasi-proportional resonant control with parameter estimation for PMSM sensorless control

Abstract

Conventional sliding mode control (SMO) utilizes a sign function and a low-pass filter (LPF) to achieve sensorless control of a permanent magnet synchronous motor (PMSM). However, the introduction of the sign function generates a large number of high harmonics, resulting in a significant system output chattering, whereas the addition of the LPF generates control delays and phase shifts. These degrade the PMSM speed and the position estimation accuracy and reduce the system control performance. To improve the performance of the PMSM, adaptive quasi-proportional resonant sensorless control with parameter estimation (AQPR_PE) is proposed. In this control model, an adaptive quasi-proportional resonance controller is used reduce the system output error and to weaken the chattering phenomenon. Meanwhile, the system PMSM parameters are estimated online and fed back to the control model to improve the system parameter robustness and the control accuracy. The Bode diagram, Popov theory, and root trajectory are used to analyze the stability of AQPR, parameter estimation, and AQPR_PE, respectively. Finally, the effectiveness of the proposed method is demonstrated by experimental validation.

Intelligent Parameter Identification and Sensorless Control of Permanent Magnet Synchronous Motor

“…Currently, the sensorless control technology of PMSM has two control methods: the high-frequency signal injection method and the method of a mathematical model based on the fundamental wave [1] . The method based on the fundamental wave mathematical model is mainly applied to medium or highspeed motors, such as the model reference adaptive method [2] , extended Kalman filter method, and SOBS method.…”