Sensor compensation in motor drives using kernel regression

L. Galotto, J. O.P. Pinto, B. Ozpineci, L. C. Leite, L. E.S. Borges

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Sensors are essential in feedback control systems, because the performance is dependent on the measurements. Fault in sensors may lead to intolerable degradation of performance and even to instability. Therefore, the high performance expected with vector control may not be achieved with fault in sensors. Several approaches related to fault tolerant motor control have already been proposed. However, most of them consider the sensors fault-free and work about faults in motors and actuators. Furthermore, the purpose of this work is not only sensor fault tolerance but also sensor fault compensation. In a standard fault tolerant approach, the fault would be detected and the sensor would be isolated. The faulted sensor may have an off-set or scaling error and could still be used if its error is compensated. In this paper, this is done with a mathematical solution based on kernel regression that can compensate the measurement error generating more accurate and reliable estimates. This technique is described and applied in motor drives. Simulated and experimental results are presented and discussed.

Original languageEnglish
Title of host publicationProceedings of 2007 IEEE International Electric Machines and Drives Conference, IEMDC 2007
Pages229-234
Number of pages6
DOIs
StatePublished - 2007
EventIEEE International Electric Machines and Drives Conference, IEMDC 2007 - Antalya, Turkey
Duration: May 3 2007May 5 2007

Publication series

NameProceedings of IEEE International Electric Machines and Drives Conference, IEMDC 2007
Volume1

Conference

ConferenceIEEE International Electric Machines and Drives Conference, IEMDC 2007
Country/TerritoryTurkey
CityAntalya
Period05/3/0705/5/07

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