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Abstract |
EKF-based Adaptation of Look-Up Tables with an Air Mass-Flow Sensor Application
A method for bias compensation and on-line map adaptation using
extended Kalman filters is developed. Key properties of the approach
include the methods of handling component aging, varying measurement
quality including operating-point-dependent reliability and
occasional outliers, and operating-point-dependent model quality.
Theoretical results about local and global observability,
specifically adapted to the map adaptation problem, are proven. In
addition, a method is presented to handle covariance growth of
locally unobservable modes, which is inherent in the map adaptation
problem. The approach is also applicable to the off-line
calibration of maps, in which case the only requirement of the data
are that the entire operating region of the system is covered,
i.e., no special calibration cycles are required. The approach is
applied to a truck engine in which an air mass-flow sensor
adaptation map is estimated during a European transient cycle. It is
demonstrated that the method manages to find a map describing the
sensor error in the presence of model errors on a measurement
sequence not specifically designed for adaptation. It is also
demonstrated that the method integrates well with traditional
engineering tools, allowing prior knowledge about specific model
errors to be incorporated and handled.
Erik Höckerdal, Erik Frisk and Lars Eriksson
Control Engineering Practice, 2011
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Last updated: 2021-11-10
