Zerdali, EmrahBarut, Murat2019-08-012019-08-0120161300-06321303-6203https://dx.doi.org/10.3906/elk-1408-136https://hdl.handle.net/11480/3789This 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.eninfo:eu-repo/semantics/openAccessInduction motorextended Kalman filtersensorless controlrotor-stator resistance estimationload torque estimationinertia estimationArticle2454525454410.3906/elk-1408-1362-s2.0-84978304049Q3247547WOS:000378097800089Q4