Inductance Estimating of Linear Switched Reluctance Motors with the Use of Adaptive Neuro-Fuzzy Inference Systems

Ferhat DALDABAN, Nurettin USTKOYUNCU
1.494 849

Abstract


 In this paper, a new method based on adaptive neuro-fuzzy inference system (ANFIS) to estimate the phase inductance of linear switched reluctance motors (LSRMs) is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the back-propagation (BP) algorithm and the least square method (LSM), is used to identify the parameters of ANFIS. The translator position and the phase current of the three-phase LSRM are used to estimate the phase inductance. The phase inductance results estimated by ANFIS are in very good agreement with the results of finite element analysis (FEA).

 Key Words: Linear Switched Reluctance Motor, ANFIS, Inductance.

 


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