Title :
A diagonally recurrent neural network approach to sensorless operation of the permanent magnet synchronous motor
Author :
Batzel, Todd D. ; Lee, Kwang Y.
Author_Institution :
Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
Abstract :
Rotor position sensorless control of the permanent magnet synchronous motor (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 is a dynamic mapping, requires fewer neurons, and converges quickly compared to feedforward and fully recurrent neural networks. Experimental results of the proposed neural observer to PMSM rotor estimation are presented
Keywords :
machine control; parameter estimation; permanent magnet motors; position control; recurrent neural nets; rotors; synchronous motors; diagonally recurrent neural network; dynamic mapping; permanent magnet synchronous motor; rotor position sensorless control; sensorless operation; Angular velocity; Artificial neural networks; Delay estimation; Motor drives; Neurons; Permanent magnet motors; Permanent magnets; Recurrent neural networks; Rotors; Sensorless control;
Conference_Titel :
Power Engineering Society Summer Meeting, 2000. IEEE
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-6420-1
DOI :
10.1109/PESS.2000.867373