Paper Info
Title
Evaluation of the Auto-Associative Neural Network Based Sensor Compensation in Drive Systems.
Abstract
The paper performs a deep analysis of the sensor drift compensation in motor drives approach presented in past publications [11-12]. In the past, the auto-associative neural networks (AANN) were found to be effective for this application. However, it is still unclear how much improvement may be obtained compared with other modeling techniques and when it is adequate to be applied. Therefore, the modeling techniques, specially the AANN, are detailed and evaluated using performance metrics. Additional experimental results in a motor drive are provided to show the compensation capability of the AANN. The feedback signals are given as the AANN inputs. The AANN then performs the auto-associative mapping of these signals so that its outputs are estimations of the sensed signals. Since the AANN exploits the physical and analytical redundancy, whenever a sensor starts to drift, the drift is compensated, and the performance of the drive system is barely affected.
Year
DOI
Venue
2008
10.1109/08IAS.2008.188
IEEE Industry Applications Society Annual Meeting
Keywords
Field
DocType
Auto-associative Neural Networks,Sensor drift compensation,Induction Motor,Drive Systems
Auto associative neural network,Data modeling,Induction motor,Electronic engineering,Control engineering,Redundancy (engineering),Content-addressable storage,Motor drive,Engineering,Artificial neural network,Machine control
Conference
ISSN
Citations 
PageRank 
0197-2618
1
0.37
References 
Authors
1
5
Name
Order
Citations
PageRank
Luigi Galotto Jr.110.37
João O. P. Pinto2308.37
Luciana Cambraia Leite310.37
Luiz Eduardo Borges da Silva4326.80
Bimal K. Bose516619.45