Bias Reduction in DAE Estimators by Model Augmentation: Observability Analysis and Experimental Evaluation
A method for bias compensation in model based estimation utilizing
model augmentation is developed. Based on a default model, that
suffers from stationary errors, and measurements from the system a low
order augmentation is estimated. The method handles models described
by differential algebraic equations and the main contributions are
necessary and sufficient conditions for the preservation of the
observability properties of the default model during the augmentation.
A characterization of possible augmentations found through the
estimation, showing the benefits of adding extra sensors during the
design, is included. This enables reduction of estimation errors also
in states not used for feedback, which is not possible with for
example PI-observers. Beside the estimated augmentation the method
handles user provided augmentations, found through e.g. physical
knowledge of the system.
The method is evaluated on a nonlinear engine model where its ability
to incorporate information from additional sensors during the
augmentation estimation is clearly illustrated. By applying the method
the mean relative estimation error for the exhaust manifold pressure
is reduced by 55%.
Erik Höckerdal, Erik Frisk and Lars Eriksson
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Last updated: 2019-08-05