TY - JOUR
T1 - An approach to sensorless operation of the permanent-magnet synchronous motor using diagonally recurrent neural networks
AU - Batzel, Todd D.
AU - Lee, Kwang Y.
N1 - Funding Information:
Manuscript received April 12, 2002; revised April 24, 2002. This work was supported in part by the Office of Naval Research under Grant N00014-00-G-0058/007. T. D. Batzel is with the Department of Computer Science and Engineering, Penn State Altoona, Altoona, PA 16601 USA (e-mail: tdbl20@psu.edu). K. Y. Lee is with the Department of Electrical Engineering, Pennsylvania State University, University Park, PA 16802 USA (e-mail: kwanglee@psu.edu). Digital Object Identifier 10.1109/TEC.2002.808386
PY - 2003/3
Y1 - 2003/3
N2 - Due to the drawbacks associated with the use of rotor position sensors in permanent-magnet synchronous motor (PMSM) drives, there has been significant interest in the so-called rotor position sensorless drive. Rotor position sensorless control of the PMSM typically requires knowledge of the PMSM structure and parameters, which in some situations are not readily available or may be difficult to obtain. Due to this limitation, an alternative approach to rotor position sensorless control of the PMSM using a diagonally recurrent neural network (DRNN) is considered. The DRNN, which captures the dynamic behavior of a system, requires fewer neurons and converges quickly compared to feedforward and fully recurrent neural networks. This makes the DRNN an ideal choice for implementation in a real-time PMSM drive system. A DRNN-based neural observer, whose architecture is based on a successful model-based approach, is designed to perform the rotor position estimation on the PMSM. The advantages of this approach are discussed and experimental results of the proposed system are presented.
AB - Due to the drawbacks associated with the use of rotor position sensors in permanent-magnet synchronous motor (PMSM) drives, there has been significant interest in the so-called rotor position sensorless drive. Rotor position sensorless control of the PMSM typically requires knowledge of the PMSM structure and parameters, which in some situations are not readily available or may be difficult to obtain. Due to this limitation, an alternative approach to rotor position sensorless control of the PMSM using a diagonally recurrent neural network (DRNN) is considered. The DRNN, which captures the dynamic behavior of a system, requires fewer neurons and converges quickly compared to feedforward and fully recurrent neural networks. This makes the DRNN an ideal choice for implementation in a real-time PMSM drive system. A DRNN-based neural observer, whose architecture is based on a successful model-based approach, is designed to perform the rotor position estimation on the PMSM. The advantages of this approach are discussed and experimental results of the proposed system are presented.
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U2 - 10.1109/TEC.2002.808386
DO - 10.1109/TEC.2002.808386
M3 - Article
AN - SCOPUS:0037350253
SN - 0885-8969
VL - 18
SP - 100
EP - 106
JO - IEEE Transactions on Energy Conversion
JF - IEEE Transactions on Energy Conversion
IS - 1
ER -