WOS: 000378097800089 ; 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. ; Scientific and Technological Research Council of Turkey (TUBITAK) [EEEAG-108E187] ; This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under research grant EEEAG-108E187.
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
2016-01-01
doi:10.3906/elk-1408-136
Article (Journal)
Electronic Resource
English
DDC: | 629 |
Enhanced Sensorless Model Predictive Control of Induction Motor Based on Extended Kalman Filter
BASE | 2020
|