Emrah Zerdali

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.

MRAS based real-time speed-sensorless control of induction motor with optimized fuzzy-PI controller

In this paper, rotor flux-oriented model reference adaptive system (RF-MRAS) based estimators are designed to obtain flux and speed estimations for speed-sensorless control of induction motors (IMs). The proposed RF-MRAS in this work replaces Conventional PI controller (CPI) in adaptation mechanism of RF-MRAS with fuzzy-PI (FPI) controller in order to improve conventional RF-MRAS. Additionally, the gains of both FPI and CPI controllers are optimized by offline via differential evolution algorithm (DEA) to make fair comparisons and without using time-consuming process of trial-and-error method.

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.

Speed-sensorless induction motor drive with unscented Kalman filter including the estimations of load torque and rotor resistance

In this paper, an unscented Kalman filter (UKF) based speed-sensorless vector control of induction motors (IMs) have been implemented for a wide speed range including zero-speed. The proposed UKF simultaneously estimates stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including viscous friction term, and rotor resistance. The effectiveness of the introduced UKF algorithm and thus the speed-sensorless IM drive are verified by computer simulations consisting of different challenging scenarios. From this point of view, it is the first speed-sensorless IM drive in the literature to utilize the UKF algorithm including the simultaneous estimations of stator currents, rotor fluxes, rotor mechanical speed, load torque including viscous friction term, and rotor resistance in simulation.

Optimization of Model Reference Adaptive System based Speed Estimation for Speed Sensorless Control of Induction Motors via Differential Evolution Algorithm

Abstract This study proposes an optimally tuned Model Reference Adaptive System (MRAS) based speed estimator using back electromotive force (EMF) vector, which does not require pure integration. The PI (Proportional and Integral) gain coefficients in the speed estimator are optimally determined by utilizing Differential Evolution (DE) algorithm. The performance of the speed estimator is tested with both simulation and real-time experiments for a wide speed range. The obtained results verify the desired performance of speed estimation.

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.