TY - GEN
T1 - Improved MRAS based HI extraction method for PMSM of Electro-Mechanical Actuator
AU - Zhang, Yujie
AU - Liu, Liansheng
AU - Liu, Datong
AU - Peng, Yu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Electro-Mechanical Actuator (EMA) utilized in the flight control actuation is becoming more and more important in aerospace applications, especially for more electric aircraft. EMA Health Indicator (HI) extraction is challenging as the sensor installation is limited, which is a critical part of EMA Prognostics and Health Management (PHM). Model Reference Adaptive System (MRAS) is an effective parameter estimation method to extract HIs of Permanent Magnet Synchronous Motor (PMSM) of EMA, which can achieve high precision estimation with a small amount of calculation. However, the MRAS based HI extraction method is not suitable for stator resistance estimation of PMSM with Field-Oriented Control (FOC) strategy in which the expected d-axis current is zero. Hence, to deal with this problem, an improved MRAS is proposed for the HI extraction of stator resistance of EMA PMSM. In the proposed HI extraction method, the expected d-axis current of PMSM is set to a constant nearly zero, and the inputs of d-axis current of MRAS is substituted by the sum of a constant and d-axis current. Besides, the structure of adjustable model of MRAS is improved to cope with this sum. Furthermore, the estimated stator resistance based on improved MRAS, which is a useful HI for EMA PMSM, can be obtained. To evaluate the effectiveness of HI extraction method based on improved MRAS for EMA PMSM, two experiments are carried out using simulation data. The experimental results demonstrate that the improved MRAS based method is more suitable for HI extraction of EMA PMSM.
AB - Electro-Mechanical Actuator (EMA) utilized in the flight control actuation is becoming more and more important in aerospace applications, especially for more electric aircraft. EMA Health Indicator (HI) extraction is challenging as the sensor installation is limited, which is a critical part of EMA Prognostics and Health Management (PHM). Model Reference Adaptive System (MRAS) is an effective parameter estimation method to extract HIs of Permanent Magnet Synchronous Motor (PMSM) of EMA, which can achieve high precision estimation with a small amount of calculation. However, the MRAS based HI extraction method is not suitable for stator resistance estimation of PMSM with Field-Oriented Control (FOC) strategy in which the expected d-axis current is zero. Hence, to deal with this problem, an improved MRAS is proposed for the HI extraction of stator resistance of EMA PMSM. In the proposed HI extraction method, the expected d-axis current of PMSM is set to a constant nearly zero, and the inputs of d-axis current of MRAS is substituted by the sum of a constant and d-axis current. Besides, the structure of adjustable model of MRAS is improved to cope with this sum. Furthermore, the estimated stator resistance based on improved MRAS, which is a useful HI for EMA PMSM, can be obtained. To evaluate the effectiveness of HI extraction method based on improved MRAS for EMA PMSM, two experiments are carried out using simulation data. The experimental results demonstrate that the improved MRAS based method is more suitable for HI extraction of EMA PMSM.
KW - ElectroMechanical Actuator
KW - Health Indicator
KW - Model Reference Adaptive System
KW - Parameter Estimation
KW - Prognostics and Health Management
UR - https://www.scopus.com/pages/publications/85078027581
U2 - 10.1109/PHM-Qingdao46334.2019.8943022
DO - 10.1109/PHM-Qingdao46334.2019.8943022
M3 - 会议稿件
AN - SCOPUS:85078027581
T3 - 2019 Prognostics and System Health Management Conference, PHM-Qingdao 2019
BT - 2019 Prognostics and System Health Management Conference, PHAI-Qingdao 2019
A2 - Guo, Wei
A2 - Li, Steven
A2 - Miao, Qiang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th Prognostics and System Health Management Conference, PHM-Qingdao 2019
Y2 - 25 October 2019 through 27 October 2019
ER -