The Design of Flux Linkage Measurement for Switched Reluctance Motor

Qiao, Dongkai; Chong-lin, Huang; Cai, Mei-qing

doi:10.1088/1742-6596/1176/5/052036

Cited by 2 publications

(1 citation statement)

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“…Literature [6][7], respectively, use sliding mode variable structure fuzzy neural network and RBF neural network to establish a magnetic chain model of the motor. Error Back Propagation Neural Network (BPNN) is connection type of feedback neural network, based on the kind of multilayer feedback networks, and compared with other neural network, it is the most widely used and versatile neural network model, which has better characteristics [8][9][10] in the aspect of classification, pattern recognition, function approximation, global convergence and generalization ability, and shows good development potential in the field of SRM modeling.…”

Section: Introductionmentioning

confidence: 99%

The Design of BP Neural Network Modeling for Switched Reluctance Motor

Qiao¹,

Cai²,

Li³

2019

Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019)

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The model parameters of 8/6 poles switched reluctance motor (SRM) were determined through using the measured magnetization curve and the establish BP neural network model, selecting Sigmoid function as the hidden layer activation function and using gradient descent method to train the network. The simulated results show that the motor flux linkage model established has a good convergence rate, higher accuracy and generalization ability. It is significant to improve the reliable running and high precision speed control of SRM motor. BP Neural Network Modeling Based on the sample shown as magnetization curve by measuring[13], we can come up with a model which aims at SRM's BP neural network magnetic linkage model.

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Section: Introductionmentioning

confidence: 99%