Novel version of bi input-extended Kalman filter for speed-sensorless control of induction motors with estimations of rotor and stator resistances, load torque, and inertia

Abstract

This study aims to develop a novel version of bi input-extended Kalman filter (BI-EKF)-based estimation technique in order to increase the number of state and parameter estimations required for speed-sensorless direct vector control (DVC) systems, which perform velocity and position controls of induction motors (IMs). For this purpose, all states required for the speed-sensorless DVC systems, besides the stator resistance $R_{s} $, the rotor resistance $R_{r}$, the load torque $t_{L}$ including the viscous friction term, and the reciprocal of total inertia $1/j_{T}$, are simultaneously estimated by the novel BI-EKF algorithm using the measured phase currents and voltages. The effectiveness of the proposed speed-sensorless DVC systems is tested by simulations under the challenging variations of $R_{s} $, $R_{r} $, $t_{L} $, $j_{T} $, and velocity/position reference. Later, the state and parameter estimations of the novel BI-EKF algorithm are confirmed with real-time experiments in a wide speed range. Finally, in both transient and steady states, a satisfactory estimation and control performance that make this study unique are achieved.