Tuning of Extended Kalman Filters for Sensorless Motion Control with Induction Motor

Risultato della ricerca: Conference contribution

Abstract

This work deals with the tuning of an Extended Kalman Filter for sensorless control of induction motors for electrical traction in automotive. Assuming that the parameters of the induction motor-load model are known, Genetic Algorithms are used for obtaining the system noise covariance matrix, considering the measurement noise covariance matrix equal to the identity matrix. It is shown that only stator currents have to be acquired for reaching this objective, which is easy to accomplish using Hall-effect transducers. In fact, the Genetic Algorithm minimizes, with respect to the system covariance matrix, a suitable measure of the displacement between the stator currents experimentally acquired and those estimated by the Kalman filter. The proposed method is validated by experiments.
Lingua originaleEnglish
Titolo della pubblicazione ospite2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive, AEIT AUTOMOTIVE 2019
Pagine1-6
Numero di pagine6
Stato di pubblicazionePublished - 2019

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All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Automotive Engineering
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

Cita questo

Sferlazza, A., D'Ippolito, F., Fagiolini, A., Raimondi, F. M., Alonge, F., & Garraffa, G. (2019). Tuning of Extended Kalman Filters for Sensorless Motion Control with Induction Motor. In 2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive, AEIT AUTOMOTIVE 2019 (pagg. 1-6)