Control Loop Sensor Calibration Using Neural Networks for Robotic Control
Joint Authors
Kramer, Kathleen A.
Stubberud, Stephen C.
Source
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-12-22
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Whether sensor model’s inaccuracies are a result of poor initial modeling or from sensor damage or drift, the effects can be just as detrimental.
Sensor modeling errors result in poor state estimation.
This, in turn, can cause a control system relying upon the sensor’s measurements to become unstable, such as in robotics where the control system is applied to allow autonomous navigation.
A technique referred to as a neural extended Kalman filter (NEKF) is developed to provide both state estimation in a control loop and to learn the difference between the true sensor dynamics and the sensor model.
The technique requires multiple sensors on the control system so that the properly operating and modeled sensors can be used as truth.
The NEKF trains a neural network on-line using the same residuals as the state estimation.
The resulting sensor model can then be reincorporated fully into the system to provide the added estimation capability and redundancy.
American Psychological Association (APA)
Kramer, Kathleen A.& Stubberud, Stephen C.. 2011. Control Loop Sensor Calibration Using Neural Networks for Robotic Control. Journal of Robotics،Vol. 2011, no. 2011, pp.1-8.
https://search.emarefa.net/detail/BIM-502816
Modern Language Association (MLA)
Kramer, Kathleen A.& Stubberud, Stephen C.. Control Loop Sensor Calibration Using Neural Networks for Robotic Control. Journal of Robotics No. 2011 (2011), pp.1-8.
https://search.emarefa.net/detail/BIM-502816
American Medical Association (AMA)
Kramer, Kathleen A.& Stubberud, Stephen C.. Control Loop Sensor Calibration Using Neural Networks for Robotic Control. Journal of Robotics. 2011. Vol. 2011, no. 2011, pp.1-8.
https://search.emarefa.net/detail/BIM-502816
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-502816