基于RBF神经网络的开关磁阻电机在线建模 及其实验验证

doi:CNKI:11-1929/V.20111231.1406.001

Abstract

Abstract: To obtain the accurate switched reluctance motor(SRM) model, the offline and online modeling schemes based on radial basis fuction (RBF) neural network are investigated in this paper. Firstly, an offline modeling scheme is studied. The flux linkage characteristics are obtained from experiment and used as a training data set, based on which an RBF neural network is trained to obtain the offline SRM model. Secondly, an online modeling method is proposed. When the operating conditions of the SRM changes, the offline model is not able to approximate the real-time SRM characteristics accurately. Based on the approximation error, an online modeling scheme is applied to improve the model accuracy by regulating the values of the RBF weights online. To verify the feasibility of this method, both the offline and online modeling schemes are tested in simulations and experiments using a 12/8 SRM. The results show that both the offline and online models can estimate the flux linkage characteristics correctly and the online model is more accurate than the offline model. Simulation and experimental results have verified the effectiveness and advantages of this modeling methods.

Key words: switched reluctance motor, radial basis function networks, offline modeling, online modeling, experimental verification

CLC Number:

CAI Yonghong, QI Ruiyun, CAI Jun, DENG Zhiquan. Online Modeling for Switched Reluctance Motor Using Radial Basis Function Neural Network and Its Experimental Validation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, (4): 705-714.

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Online Modeling for Switched Reluctance Motor Using Radial Basis Function Neural Network and Its Experimental Validation

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