Sensorless Predictive Current Control of a Permanent Magnet Synchronous Motor Powered by a Three-Level Inverter

Abstract

Permanent magnet synchronous motors and their relevant control techniques have become more and more prevalent in electric vehicle driving applications because of their outstanding performance. This paper studies a simple and effective sensorless scheme based on a current observer for a permanent magnet synchronous motor powered by a three-level inverter, which avoids the injection of a high-frequency signal and the observation of back-electromotive force. In this way, a current observer is constructed to observe d–q-axes currents by relying on an extended-current model. Thereafter, the position and speed of the machine can be extracted from two PI controllers associated with the d–q-axes current-tracking errors. Meanwhile, it takes into account the model predictive current control with neutral-point voltage balance to maintain the stability of the three-level inverter system. In general, this scheme realizes sensorless operation in a full-speed domain and is no longer limited by the types of inverter and method used.

Inductance Estimation Based on Wavelet-GMDH for Sensorless Control of PMSM

“…However, practical issues such as installation feasibility, high sensor costs, and control failures in case of sensor malfunctions have led to active research on sensorless control methods for estimating the position and speed of the rotor. Sensorless control techniques mainly consist of two approaches: current model-based methods and extended back electromotive force (EMF) based methods [1][2][3][4][5][6][7][8][9]. Recently, there has been a significant adoption of extended EMF based methods, which offer unlimited input variables and rapid response capabilities, due to the arctangent operation achievable with microprocessor technology.…”

Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent

“…The relatively good performance of these types of controllers is well-known, but when significant parametric variations occur, the performance of these types of control systems, based on PI-type controllers, decreases significantly. A number of PMSM control systems have been developed and adapted, including adaptive [9,10], optimal [11,12] and predictive [13,14], but also fuzzy [15,16] and neuro-fuzzy control systems have been invented with good results [17,18]. A backstepping controller for IM is developed in [19].…”