Improved Analytical Nonlinear Model for Switched Reluctance Motor Using Gaussian Distribution Probability Density Function

In this manuscript, a new nonlinear analytical model of switched reluctance motor based on Gaussian distribution probability density function is proposed. In this model, inductance profile of the switched reluctance motor is considered as a Gaussian function which is a nonlinear function in terms of phase current and rotor position. Extracted inductance data from a switched reluctance motor prototype are used to determine the proposed nonlinear analytical model. Simulations are firstly performed to verify the effectiveness of the proposed Gaussian model in a simple control scheme. In addition, measured data acquired from an experimental implementation of the control scheme for the switched reluctance motor prototype are also used to validate the proposed model. During the validation, experimental results on the motor prototype with DSP-based digital controller (TMS320F28335) are achieved under various rotor positions and currents in low and rated speed. The outcome is then compared with a previously developed polynomial inductance model. Comparing the achieved results, through the proposed nonlinear analytical model and those of the polynomial inductance model, it is seen that the model provides a high degree of accuracy while it is involved with fewer coefficients.