6533b82afe1ef96bd128ccc2
RESEARCH PRODUCT
Tuning of Extended Kalman Filters for Sensorless Motion Control with Induction Motor
Francesco AlongeFrancesco Maria RaimondiAntonino SferlazzaGiovanni GarraffaAdriano FagioliniFilippo D'ippolitosubject
Computer scienceCovariance matrixStator020209 energy020208 electrical & electronic engineeringIdentity matrix02 engineering and technologyKalman filterMotion controllaw.inventionExtended Kalman filterExtended Kalman filterNoiseGenetic algorithmSettore ING-INF/04 - AutomaticaControl theorylawSenseless controlElectrical traction0202 electrical engineering electronic engineering information engineeringInduction motordescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-07-01 |