Stator Resistance Estimation Using Adaptive Estimation via a Bank of Kalman Filters

Authors

Alia R. Strandt, Marquette University
Andrew Strandt, Marquette University
Susan C. Schneider, Marquette UniversityFollow
Edwin E. Yaz, Marquette UniversityFollow

Document Type

Article

Language

eng

Publication Date

8-16-2018

Publisher

Institute of Electrical and Electronic Engineers (IEEE)

Source Publication

2018 Annual American Control Conference (ACC)

Source ISSN

2378-5861

Abstract

Accurate and efficient control of electric motors is dependent on knowledge of motor parameters such as the resistance and the inductance of the winding. However, these parameters are often unavailable to the control designer because they are dependent on the motor design and may change due to environmental effects such as temperature. An accurate real-time method to determine the values of these unknown parameters can improve motor performance over the entire operating range. In this work, a parameter estimation technique based on a bank of Kalman filters is used to adaptively estimate the motor winding resistance. Simulation results for a 3.5 horsepower interior permanent magnet (IPM) synchronous motor operating at rated torque demonstrate that this technique may be used for real-time estimation of motor parameters.

Comments

Accepted version. 2018 Annual American Control Conference (ACC), (August 16, 2018): 1078-1083. DOI. © 2018 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.

Recommended Citation

Strandt, Alia R.; Strandt, Andrew; Schneider, Susan C.; and Yaz, Edwin E., "Stator Resistance Estimation Using Adaptive Estimation via a Bank of Kalman Filters" (2018). Electrical and Computer Engineering Faculty Research and Publications. 629.
https://epublications.marquette.edu/electric_fac/629