Murat Barut

Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters

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

EKF based estimation for direct vector control of induction motors

In this study an EKF algorithm is developed to be used for the direct vector control of induction motors. The algorithm involves the estimation of the rotor resistance, rotor flux, angular velocity and load torque in addition to the stator currents measured as output. Simulation results demonstrate a good performance and robustness.

An extended Kalman filter based sensorless direct vector control of induction motors

In this study, it is aimed to design a rotor-oriented sensorless direct vector control for the speed control of an induction motor (IM). For this purpose, all the states required for direct vector control in addition to the step-shaped load torque and rotor resistance are estimated using extended Kalman Filter (EKF). Simulation results demonstrate a good performance and robustness to variations of load and rotor resistance.

The Comparisons of Optimized Extended Kalman Filters for Speed-Sensorless Control of Induction Motors

This paper presents the comparisons of optimized extended Kalman filters (EKFs) using different fitness functions for speed-sensorless vector control of induction motors (IMs). In order to achieve high performance estimations of states/parameter by EKF algorithm, state and noise covariance matrices must be accurately selected. For this aim, instead of using time-consuming trial-and-error method to determine those covariance matrices, in this paper EKF algorithm is optimized by differential evolution algorithm (DEA) and multi-objective DEA (MODEA) with the utilization of different fitness functions. The optimally obtained set of each covariance matrices is used in EKF algorithm built on the same IM model and thus, the estimation results of the optimized EKF algorithms are compared in real-time experiments in order to conclude which fitness function is better for motion control applications.

Sensorless‐estimation of induction motors in wide speed range

Purpose – The purpose of this paper is to improve performance in the estimation of velocity and flux in the sensorless control of induction motors (IMs) over a wide speed range, including low and zero speed.Design/methodology/approach – Temperature and frequency dependent variations of stator (Rs) and rotor (R′r) resistances are very effective on estimation performance in sensorless control over a wide speed range. To this aim, an extended Kalman filter (EKF) is designed, which estimates the stator resistance, Rs, load torque, tL, velocity and flux. To provide robustness against R′r variations, the extended model is also continuously updated with R′r values from a look‐up table, built via EKF estimation.Findings – As demonstrated by the experimental results, the estimated states and parameters undergo a very short transient and attain their steady‐state values accurately, with no need for signal injection due to the inherent noise introduced by EKF.Originality/value – The value of this study is in the dev...

Sensorless Sliding Mode Position Control of Induction Motors Using Braided Extended Kalman Filters

This study is aimed at designing a sensorless sliding mode position control system for the rotor flux oriented Direct Vector Control (DVC) strategy of induction motors (IMs). For this purpose, a novel sliding mode controller (SMC) with reduced-chattering is designed for the control of the flux and angular position of the motor. All the states required for DVC in addition to the step-shaped load torque, stator resistance and rotor resistance are estimated using Braided EKF based observers. The performance of the new SMC is compared against a previously developed chattering-free SMC scheme. The simulation results demonstrate an improved robustness in the system response against parameter and load variations. It has also been demonstrated that the new Braided EKF technique used in the proposed sliding mode position control system also increases estimation accuracy of estimations when compared with chattering-free SMC under challenging variations of 100 % in the load and stator & rotor resistance.

EKF based rotor and stator resistance estimations for direct torque control of Induction Motors

This study presents the direct torque controlled induction motor (IM) drive utilizing a novel extended Kalman filter (EKF) that simultaneously estimates stator stationary axis components of stator currents and stator fluxes in addition to rotor and stator resistances with the assumption of available stator voltages/currents and rotor speed. Thus, it is desired to show that the on-line estimations of rotor and stator resistances are possible by using a single EKF algorithm in the case with speed-sensor. Performances of the proposed EKF are tested under challenging scenarios generated in simulations. The obtained results confirm very satisfying performances of the introduced EKF algorithm and thus the IM drive.

Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control

This paper presents a novel hybrid estimator consisting of an extended Kalman filter (EKF) and an active power-based model reference adaptive system (AP-MRAS) in order to solve simultaneous estimation problems of the variations in stator resistance ( R s ) and rotor resistance ( R r ) for speed-sensorless induction motor control. The EKF simultaneously estimates the stator stationary axis components ( i s α and i s β ) of stator currents, the stator stationary axis components ( φ s α and φ s β ) of stator fluxes, rotor angular velocity ( ω m ), load torque ( t L ) and R r , while the AP-MRAS provides the online R s estimation to the EKF. Both the AP-MRAS, whose adaptation mechanism is developed with the help of the least mean squares method in this paper, and the EKF only utilize the measured stator voltages and currents. Performances of the proposed hybrid estimator in this paper are tested by challenging scenarios generated in simulations and real-time experiments. The obtained results demonstrate the effectiveness of the introduced hybrid estimator, together with a 22 . 87 % reduction in the processing time and size of the estimation algorithm in terms of previous studies performing the same estimations of the states and parameters. From this point of view, it is the first such study in the literature, to our knowledge.

Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors

Abstract This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high performance speed-sensorless control of induction motors (IMs). The proposed estimator simultaneously performs the estimations of stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including the viscous friction term, and reciprocal of total inertia by using measured stator phase currents and voltages. The inertia estimation is done since it varies with the load coupled to the shaft and affects the performance of speed estimation especially when the rotor speed changes. In this context, the estimations of all mechanical state and parameters besides flux estimation required for high performance control methods are performed together. The performance of the proposed estimator is tested by simulation and real-time experiments under challenging variations in load torque and velocity references; and in both transient and steady states, the quite satisfactory estimation performance is achieved.

Load torque and stator resistance estimations with unscented Kalman filter for speed-sensorless control of induction motors

In this study, speedsensorless IM drive based on unscented Kalman filter (UKF) with the online estimations of stator stationary axis components of stator currents, rotor fluxes, rotor mechanical speed, load torque including the friction term, and stator resistance is designed. Therefore, the proposed speed-sensorless IM drive is robust to load torque and stator resistance changes. Different challenging scenarios including ramp- and step-type variations in load torque and stator resistance at both zero and high speeds are performed in computer simulations to demonstrate the superiority of the proposed UKF based speedsensorless drive.