Recurrent Neural Network Control for a Synchronous Reluctance Motor
碩士 === 國立雲林科技大學 === 電機工程系碩士班 === 101 === This thesis develops a digital signal processor (dSPACE inc. DS1104) based synchronous reluctance motor (SynRM) drive system. Elman neural network and modified Elman neural network controller are proposed in the SynRM when the SynRM has parameters variations...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2013
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| Online Access: | http://ndltd.ncl.edu.tw/handle/21986022062786916763 |
| Summary: | 碩士 === 國立雲林科技大學 === 電機工程系碩士班 === 101 === This thesis develops a digital signal processor (dSPACE inc. DS1104) based synchronous reluctance motor (SynRM) drive system. Elman neural network and modified Elman neural network controller are proposed in the SynRM when the SynRM has parameters variations and external disturbances. Recurrent Neural Network (RNN) and Elman neural network (ENN) are compared which ENN has faster convergence for special recurrent structure. The on-line parameters learning of the neural network used the back-propagation (BP) algorithm. We use the discrete-type Lyapunov function to guarantee the output error convergence. Finally, the proposed controller algorithms are shown in experimental results effectively.
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