scholarly journals Non‐linear model of the switched reluctance motor with included core losses’ effects

2021 ◽  
Author(s):  
Dragan S. Mihic ◽  
Mladen V. Terzic ◽  
Zarko V. Koprivica
Keyword(s):  
Linear Model ◽  
Switched Reluctance Motor ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Non Linear ◽  
Core Losses

scholarly journals Performance Tests of a PI Speed Controller Applied in a Non-linear Model of a Switched Reluctance Motor 8/6

2021 ◽  
Vol 16 ◽  
pp. 508-518
Author(s):  
J. D. González-San Román ◽  
J. U. Liceaga-Castro ◽  
I. I. Siller-Alcalá ◽  
E. Campero-Littlewood
Keyword(s):  
Linear Model ◽  
Viscous Friction ◽  
Switched Reluctance Motor ◽  
Performance Tests ◽  
Switched Reluctance ◽  
Speed Controller ◽  
Reluctance Motor ◽  
Non Linear ◽  
Point Set ◽  
Inverter Circuit

This article presents the performance tests, by simulation, of a classic PI speed controller applied to a switched reluctance motor (SRM) 8/6. The motor is represented by a linearization of the simplified non-linear model at an operating point set at 2000rpm. The model includes Coulomb plus viscous friction nonlinearity and an ideal inverter circuit. The control system simulations that are carried out are divided into two types: regulation tests and tracking tests, all simulations are carried out in Matlab® / Simulink software.

scholarly journals MNLR and ANFIS Based Inductance Profile Estimation for Switched Reluctance Motor

2021 ◽  
Author(s):  
Susitra Dhanarajalu
Keyword(s):  
Regression Analysis ◽  
Fuzzy Inference ◽  
Simulated Data ◽  
Switched Reluctance Motor ◽  
Simulation Software ◽  
Accurate Estimation ◽  
Computationally Efficient ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Non Linear

This chapter aims in presenting the methods for the accurate estimation of highly non linear phase inductance profile of a switched reluctance motor (SRM). The magnetization characteristics of a test SRM is derived from the SRDaS (Switched Reluctance Design and Simulation) simulation software. Statistical interpolation based regression analysis and Artificial Intelligence (AI) techniques are used for developing the computationally efficient inductance model. Multi Variate Non linear Regression (MVNLR) from the class of regression analysis and Adaptive Neuro Fuzzy Inference System (ANFIS) under the class of AI are implemented and tested on the simulated data. Non linear Inductance profile L(I,θ) of SRM is successfully estimated for the complete working range of phase currents (Iph). At each Iph, L(I,θ) values are estimated and presented for one cycle of rotor position (θ). Estimated inductance profile based on the two proposed methods is observed to be in excellent correlation with the true value of data.

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