제어로봇시스템학회:학술대회논문집
- 2005.06a
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- Pages.1478-1481
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- 2005
The Speed Control and Estimation of IPMSM using Adaptive FNN and ANN
- Lee, Hong-Gyun (Department of Electrical Control Engineering, Sunchon National University) ;
- Lee, Jung-Chul (Department of Electrical Control Engineering, Sunchon National University) ;
- Nam, Su-Myeong (Department of Electrical Control Engineering, Sunchon National University) ;
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Choi, Jung-Sik
(Department of Electrical Control Engineering, Sunchon National University) ;
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Ko, Jae-Sub
(Department of Electrical Control Engineering, Sunchon National University) ;
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Chung, Dong-Hwa
(Department of Electrical Control Engineering, Sunchon National University)
- Published : 2005.06.02
Abstract
As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.