Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters

Abstract

In this paper, extended-Kalman-filter-based estimation algorithms that could be used in combination with the speed-sensorless field-oriented control and direct-torque control of induction motors (IMs) are developed and implemented experimentally. The algorithms are designed aiming minimum estimation error in both transient and steady state over a wide velocity range, including very low and persistent zero-speed operation. A major challenge at very low and zero speed is the lost coupling effect from the rotor to the stator, which makes the information on rotor variables unobservable on the stator side. As a solution to this problem, in this paper, the load torque and the rotor angular velocity are simultaneously estimated, with the velocity taken into consideration via the equation of motion and not as a constant parameter, which is commonly the case in most past studies. The estimation of load torque, on the other hand, is performed as a constant parameter to account for Coulomb and viscous friction at steady state to improve the estimation performance at very low and zero speed. The estimation algorithms developed based on the rotor and stator fluxes are experimentally tested under challenging variations and reversals of the velocity and load torque (step-type and varying linearly with velocity) over a wide velocity range and at zero speed. In all the scenarios, the current estimation error has remained within a very narrow error band, also yielding acceptable velocity estimation errors, which motivate the use of the developed estimation method in sensorless control of IMs over a wide velocity range and persistent zero-speed operation.Index Terms-Extended Kalman filter (EKF), induction motor (IM), low/zero-speed operation, sensorless control.

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“…In [27], the EKF algorithms were applied in speed sensorless control of IMs by combining field-oriented control (FOC) and direct-torque control (DTC). Additionally, a braided EKF was proposed for sensorless control of IMs in [28], and challenging parameter and load variations in a wide speed range were considered to verify the algorithm.…”

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“…For example, the rotor speed and position can be estimated based on the stator voltage equation of the AC motor [3], reference model of the AC motor [4], state observer [5], back EMF [6], the Kalman filtering [7], nonlinear control [8], signal injection [9] and fuzzy control [10].…”

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“…Another study, which relies on the use of Extended Kalman Filter, implements the velocity estimation Paper template for Automatika Eray A. Baran, Edin Golubovic, Asif Sabanovic with current and DC voltage inputs of an induction motor [4]. A more recent example of Extended Kalman Filtering on velocity estimation can be found in [15]. On the other hand, the major problem about the tuning of Kalman Filter parameters makes it difficult to use in many applications.…”