DocumentCode :
1534106
Title :
Direct self control of induction motor based on neural network
Author :
Shi, K.L. ; Chan, T.F. ; Wong, Y.K. ; Ho, S.L.
Author_Institution :
Dept. of Electr. Eng., Hong Kong Polytech., Kowloon, Hong Kong
Volume :
37
Issue :
5
fYear :
2001
Firstpage :
1290
Lastpage :
1298
Abstract :
This paper presents an artificial-neural-network-based direct-self-control (ANN-DSC) scheme for an input inverter-fed three-phase induction motor. In order to cope with the complex calculations required in direct self control (DSC), the proposed artificial-neural-network (ANN) system employs the individual training strategy with fixed-weight and supervised models. A computer simulation program is developed using Matlab/Simulink together with the Neural Network Toolbox. The simulated results obtained demonstrate the feasibility of ANN-DSC. Compared with the classical digital-signal-processor-based DSC, the proposed ANN-based scheme incurs much shorter execution times and, hence, the errors caused by control time delays are minimized
Keywords :
digital simulation; electric machine analysis computing; induction motors; invertors; learning (artificial intelligence); machine control; neurocontrollers; self-adjusting systems; ANN; Matlab/Simulink; Neural Network Toolbox; artificial-neural-network-based direct-self-control; computer simulation program; control time delays; direct self control; errors minimisation; fixed-weight models; induction motor; input inverter-fed three-phase induction motor; neural network; supervised models; training strategy; Artificial neural networks; Computational modeling; Computer networks; Control systems; Delay effects; Error correction; Induction motors; Industry Applications Society; Mathematical model; Neural networks;
fLanguage :
English
Journal_Title :
Industry Applications, IEEE Transactions on
Publisher :
ieee
ISSN :
0093-9994
Type :
jour
DOI :
10.1109/28.952504
Filename :
952504
Link To Document :
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