Model Predictive Current Control With Online Parameter Estimation for Synchronous Reluctance Machine Controlled by High-Frequency Signal Injection Position-Sensorless
Hyeonseong Kim, Kibok Lee
Abstract
Accurate machine parameters and rotor position information are essential in vector-controlled motor drive systems. However, machine parameter variations by various factors such as the current and the temperature degrade the performance of vector control. Also, a position sensor such as an encoder and a resolver increases the drive system cost. This paper proposes model predictive current control (MPCC) with the online parameter estimation for synchronous reluctance machines controlled by a high-frequency signal injection position-sensorless method. This approach removes the need for accurate knowledge about the system and eliminates the need for the position sensor. The proposed method adopts a recursive least-square (RLS) to estimate the electrical machine parameters in real-time. The estimated parameters are used for the deadbeat continuous control set (CCS) MPCC and the position-sensorless control. The high-frequency signal injection method is modified to be suitable for the proposed CCS-MPCC method, ensuring stable operation in the low-speed regions. Simulation and experimental results are provided to verify the performance of the proposed control method.