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    Wyświetlanie 1-4 z 4
    Tytuł:
    Introduction to the Special Section on State and Parameter Estimation Methods for Sensorless Drives
    Autorzy:
    Barut, M.
    Hinkkanen, M.
    Orlowska-Kowalska, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/1193677.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    sensorless control
    extended Kalman filter
    unscented Kalman filter
    MRAS estimators
    neural networks
    Opis:
    This short article constitutes an introductory part of the Special Section (SS) on State and Parameter Estimation Methods in Sensorless Drives. In the current issue of the journal, the first part of this section is published. Accepted articles are focussed mainly on estimation of the state variables and parameters for vector-controlled induction motor (IM) drives, using different concepts, such as different types of Kalman filters (KFs) and model reference adaptive systems (MRASs). The KF was also proposed for brushless DC motor (BLDC). Also, neural networks (NNs) have been proposed for mechanical state variables’ estimation of the drive system with elastic couplings.
    Źródło:
    Power Electronics and Drives; 2018, 3, 38; 111-113
    2451-0262
    2543-4292
    Pojawia się w:
    Power Electronics and Drives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Simulation analysis of extended Kalman filter applied for estimating position and speed of a brushless DC motor
    Autorzy:
    Chojowski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1193591.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    extended Kalman filter
    brushless DC motor
    covariance matrix
    sensorless control
    BLDC
    Opis:
    The purpose of this paper was to present a method for the estimation of the rotor speed and position of brushless DC (BLDC) motor. The BLDC motor state equations were developed, and the model was discretised. Extended Kalman filter has been designed to observe specific states from the state vector, needed for the sensorless control (rotor position) and to determine the speed, which may be useful to use as a feedback for the controller. A test was carried out to determine the noise covariance matrices in a simulation manner.
    Źródło:
    Power Electronics and Drives; 2018, 3, 38; 145-155
    2451-0262
    2543-4292
    Pojawia się w:
    Power Electronics and Drives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Extended Kalman filter based speed-sensorless load torque and inertia estimations with observability analysis for induction motors
    Autorzy:
    Zerdali, E.
    Barut, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1193483.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    extended Kalman filter
    induction motor
    load torque estimation
    inertia estimation
    speed-sensorless control
    observability analysis
    Opis:
    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.
    Źródło:
    Power Electronics and Drives; 2018, 3, 38; 115-127
    2451-0262
    2543-4292
    Pojawia się w:
    Power Electronics and Drives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamic performance of estimator-based speed sensorless control of induction machines using extended and unscented Kalman filters
    Autorzy:
    Horváth, K.
    Kuslits, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1193590.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    induction machine
    speed sensorless control
    field-oriented control
    FOC
    non-linear state estimation
    load torque estimation
    extended Kalman filter
    EKF
    unscented Kalman filter
    UKF
    Opis:
    This paper presents an estimator-based speed sensorless field-oriented control (FOC) method for induction machines, where the state estimator is based on a self-contained, non-linear model. This model characterises both the electrical and the mechanical behaviours of the machine and describes them with seven state variables. The state variables are estimated from the measured stator currents and from the known stator voltages by using an estimator algorithm. An important aspect is that one of the state variables is the load torque and, hence, it is also estimated by the estimator. Using this feature, the applied estimator-based speed sensorless control algorithm may be operated adequately besides varying load torque. In this work, two different variants of the control algorithm are developed based on the extended and the unscented Kalman filters (EKF, UKF) as state estimators. The dynamic performance of these variants is tested and compared using experiments and simulations. Results show that the variants have comparable performance in general, but the UKF-based control provides better performance if a stochastically varying load disturbance is present.
    Źródło:
    Power Electronics and Drives; 2018, 3, 38; 129-144
    2451-0262
    2543-4292
    Pojawia się w:
    Power Electronics and Drives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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