Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor

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    73 Citazioni (Scopus)

    Abstract

    This paper deals with convergence analysis of the Extended Kalman Filters (EKF) for sensorless motion control systems with induction motor. An EKF is tuned according to a six–order discrete–time model of the induction motor, affected by system and measurement noises, obtained by applying a first–order Euler discretization to a six–order continuous–time model. Some properties of the discrete–time model have been explored. Among these properties it is relevant the observability property, which leads to conditions that can be directly linked with the working conditions of the machine. Starting from these properties, the convergence of the stochastic state estimation process, in mean square sense, has been shown. The convergence is also explored with reference to the difference between the samples of the state of the continuous–time model and that estimated by the EKF. The results achieved theoretically have been also validated by means of experimental tests carried out on an IM prototype.
    Lingua originaleEnglish
    pagine (da-a)2341-2352
    Numero di pagine12
    RivistaIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
    Volume62
    Stato di pubblicazionePublished - 2015

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    Extended Kalman filters
    Induction motors
    Observability
    Motion control
    State estimation
    Control systems
    Sensorless control

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Electrical and Electronic Engineering
    • Computer Science Applications

    Cita questo

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    title = "Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor",
    abstract = "This paper deals with convergence analysis of the Extended Kalman Filters (EKF) for sensorless motion control systems with induction motor. An EKF is tuned according to a six–order discrete–time model of the induction motor, affected by system and measurement noises, obtained by applying a first–order Euler discretization to a six–order continuous–time model. Some properties of the discrete–time model have been explored. Among these properties it is relevant the observability property, which leads to conditions that can be directly linked with the working conditions of the machine. Starting from these properties, the convergence of the stochastic state estimation process, in mean square sense, has been shown. The convergence is also explored with reference to the difference between the samples of the state of the continuous–time model and that estimated by the EKF. The results achieved theoretically have been also validated by means of experimental tests carried out on an IM prototype.",
    author = "Francesco Alonge and Filippo D'Ippolito and Adriano Fagiolini and Antonino Sferlazza and Tommaso Cangemi",
    year = "2015",
    language = "English",
    volume = "62",
    pages = "2341--2352",
    journal = "IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS",

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    TY - JOUR

    T1 - Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor

    AU - Alonge, Francesco

    AU - D'Ippolito, Filippo

    AU - Fagiolini, Adriano

    AU - Sferlazza, Antonino

    AU - Cangemi, Tommaso

    PY - 2015

    Y1 - 2015

    N2 - This paper deals with convergence analysis of the Extended Kalman Filters (EKF) for sensorless motion control systems with induction motor. An EKF is tuned according to a six–order discrete–time model of the induction motor, affected by system and measurement noises, obtained by applying a first–order Euler discretization to a six–order continuous–time model. Some properties of the discrete–time model have been explored. Among these properties it is relevant the observability property, which leads to conditions that can be directly linked with the working conditions of the machine. Starting from these properties, the convergence of the stochastic state estimation process, in mean square sense, has been shown. The convergence is also explored with reference to the difference between the samples of the state of the continuous–time model and that estimated by the EKF. The results achieved theoretically have been also validated by means of experimental tests carried out on an IM prototype.

    AB - This paper deals with convergence analysis of the Extended Kalman Filters (EKF) for sensorless motion control systems with induction motor. An EKF is tuned according to a six–order discrete–time model of the induction motor, affected by system and measurement noises, obtained by applying a first–order Euler discretization to a six–order continuous–time model. Some properties of the discrete–time model have been explored. Among these properties it is relevant the observability property, which leads to conditions that can be directly linked with the working conditions of the machine. Starting from these properties, the convergence of the stochastic state estimation process, in mean square sense, has been shown. The convergence is also explored with reference to the difference between the samples of the state of the continuous–time model and that estimated by the EKF. The results achieved theoretically have been also validated by means of experimental tests carried out on an IM prototype.

    UR - http://hdl.handle.net/10447/137492

    M3 - Article

    VL - 62

    SP - 2341

    EP - 2352

    JO - IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

    JF - IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

    ER -