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ANN과 FMRLC를 이용한 유도전동기의 센서리스 속도제어
남수명(Su-Myeong Nam),이정철(Jung-Chul Lee),이홍균(Hong-Gyun Lee),이영실(Young-Sil Lee),박병상(Bung-Sang Park),정동화(Dong-Hwa Chung) 전력전자학회 2004 전력전자학술대회 논문집 Vol.- No.-
Artificial intelligence control that use Fuzzy, Neural network, genetic algorithm etc. in the speed control of induction motor recently is studied much. Also, sensors such as Encoder and Resolver are used to receive the speed of induction motor and information of position. However, this control method or sensor use receives much effects in urroundings environment change and react sensitively to parameter change of electric motor and control Performance drops. Presume the speed and position of induction motor by ANN in this treatise, and because using FMRLC that is consisted of two Fuzzy Logic, can correct Fuzzy Rule Base through learning and save good response special quality in change of condition such as change of parameter.
이영실(Young-Sil Lee),이정철(Jung-Chul Lee),이홍균(Hong-Gyun Lee),남수명(Su-Myeong Nam),김종관(Jong-Gwan Kim),박병상(Bung-Sang Park),정동화(Dong-Hwa Chung) 전력전자학회 2004 전력전자학술대회 논문집 Vol.- No.-
This paper proposes a new method of on-line estimation for rotor resistance of the induction motor in the indirect vector controlled drive, using artificial neural network (ANN). The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and actual state variable of a neural network model is back propagated to adjust the weight of a neural network model, so that the actual state variable tracks the desired value. The performance of rotor resistance estimator and torque and flux responses of drive, together with these estimators, are investigated variations rotor resistance from their nominal values. The rotor resistance are estimated analytically, using the proposed ANN in a vector controlled induction motor drive.
이정철(Jung-Chul Lee),이홍균(Hong-Gyun Lee),이영실(Young-Sil Lee),남수명(Su-Myeong Nam),김종관(Jong-Kwan Kim),박병상(Bung-Sang Park),정동화(Dong-Hwa Chung) 전력전자학회 2004 전력전자학술대회 논문집 Vol.- No.-
This paper is proposed genetic algorithm(GA) based on the vector controlled induction motor drive system. This paper employed GA to the classical PI controller. The approach having ability for global optimization and with good robustness, is expected to overcome some weakness of conventional approaches and to be more acceptable for industrial practices. Also, this paper is proposed estimation of speed of induction motor using ANN 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 theoretical analysis as well as the simulation results to verify the effectiveness of the new method.
ANN-PE에 의한 IPMSM 드라이브의 센서리스 제어
이홍균(Hong-Gyun Lee),이정철(Jung-Chul Lee),이영실(Young-Sil Lee),남수명(Su-Myeong Nam),김종관(Jong-Gwan Kim),박병상(Bung-Sang Park),정동화(Dong-Hwa Chung) 전력전자학회 2004 전력전자학술대회 논문집 Vol.- No.-
Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of speed estimation and control for IPMSM using adaptive neural network. The hybrid combination of neural network and adaptive fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using adaptive neural network fuzzy(A-NNF) and estimation of speed using artificial neural network-parameter estimator(ANN-PE) controller. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the hybrid intelligent control and ANN-PE