Linear regression position estimation methods for switched reluctance motors

Abstract

In this research different sensorless methods that could reliably help to remove the need to use position sensors in switched reluctance motors(SRM) are developed. These methods are different to the conventional ones by providing unique rotor positions without relying on premeasured motor magnetic characteristics. The approach is systematic and mathematical. The relationship among motor phase inductance profiles is investigated and three types of functions to model their relationship are presented. Through regression analysis the function coefficients are found. Methods to linearize these functions for regression are explained. These coefficients are then used to calculate the rotor positions at standstill or flying restart. The estimation accuracies and efficiencies of the methods using the three functions are compared. These methods are collectively called the Linear Regression Position Estimation Methods(LRPEM). Two methods for position estimation on a running motor are also developed. A special event called Adjacent Phase Inductance Profiles Crossing(APC) when an SRM is running is used to turn the motor into a low resolution encoder. The speed and position values obtained from this encoder are fed to a Kalman filter where detailed rotor position values are produced. The low resolution encoder is used to commutate the phases with limited speed and torque capability, while the high resolution outputs from the Kalman filter are used for fine commutations. The two commutation methods are compared and the effects from mutual couplings, voltage drops and magnetic saturation are shown.