DocumentCode :
3157454
Title :
Neural networks Implementation of Direct Torque Control of Permanent Magnet Synchronous Motor
Author :
Chunmei, Zhang ; Baozhu, Ma ; Heping, Liu ; Shujin, Chen
Author_Institution :
Inf. Eng. Sch., Univ. of Sci. & Technol. Beijing, Beijing
Volume :
2
fYear :
2006
fDate :
4-6 Oct. 2006
Firstpage :
1839
Lastpage :
1843
Abstract :
Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. This paper discusses the application of neural networks to control interior permanent magnet synchronous motor using direct torque control (DTC). A neural network is used to emulate the state selector of the DTC. The neural networks used in this paper are the back-propagation, radial basis function. In order to reduce the training patterns and increase the execution speed of the training process, the inputs of switching table is converted to digital signals, i.e., one bit represent the flux error, one bit the torque error and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quickly parallel speed and high torque response.
Keywords :
backpropagation; electric machine analysis computing; machine control; neurocontrollers; permanent magnet motors; radial basis function networks; synchronous motors; torque control; back-propagation; digital signals; direct torque control; flux error; learning speed; neural networks implementation; permanent magnet synchronous motor; radial basis function networks controller; stability; stator flux; switching table; weight convergence; Computer errors; Computer simulation; Convergence; Neural networks; Permanent magnet motors; Signal processing; Stability; Stators; Synchronous motors; Torque control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Engineering in Systems Applications, IMACS Multiconference on
Conference_Location :
Beijing
Print_ISBN :
7-302-13922-9
Electronic_ISBN :
7-900718-14-1
Type :
conf
DOI :
10.1109/CESA.2006.4281937
Filename :
4281937
Link To Document :
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