TY - GEN
T1 - Sensor compensation in motor drives using kernel regression
AU - Galotto, L.
AU - Pinto, J. O.P.
AU - Ozpineci, B.
AU - Leite, L. C.
AU - Borges, L. E.S.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=35048902417&partnerID=8YFLogxK
U2 - 10.1109/IEMDC.2007.383582
DO - 10.1109/IEMDC.2007.383582
M3 - Conference contribution
AN - SCOPUS:35048902417
SN - 1424407435
SN - 9781424407439
T3 - Proceedings of IEEE International Electric Machines and Drives Conference, IEMDC 2007
SP - 229
EP - 234
BT - Proceedings of 2007 IEEE International Electric Machines and Drives Conference, IEMDC 2007
T2 - IEEE International Electric Machines and Drives Conference, IEMDC 2007
Y2 - 3 May 2007 through 5 May 2007
ER -