6533b7d9fe1ef96bd126cbad
RESEARCH PRODUCT
Sensorless control of induction motors using an extended Kalman filter and linear quadratic tracking
David HalandVan Khang HuynhSurya Teja Kandukurisubject
010302 applied physicsEngineeringbusiness.industryRotor (electric)Control engineering02 engineering and technologyConvertersOptimal control01 natural scienceslaw.inventionExtended Kalman filterControl theoryPosition (vector)law0103 physical sciences0202 electrical engineering electronic engineering information engineeringTorque020201 artificial intelligence & image processingbusinessInduction motordescription
Induction motors are the most commonly used prime-movers in industrial applications. Many induction motors supplied by frequency converters are coupled with a physical angular rotor position/velocity sensor which makes the drive complex and require maintenance. This paper presents a sensorless control structure to avoid using a physical angular rotor position/velocity sensor. The proposed method estimates and control the angular rotor velocity using optimal control theory. The optimal controller used in this paper is based on linear quadratic tracking and the states of the machine are estimated using an extended Kalman filter. Both the controller and the estimator utilize the same internal model, based on the direct-quadrature axis approach. The effectiveness of the proposed method is numerically investigated by simulations and further analyzed with experimental data extracted from a commercial frequency converter using field-oriented control.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-08-01 | 2017 20th International Conference on Electrical Machines and Systems (ICEMS) |