Observer Design and Model Augmentation for Bias Compensation With a Truck Engine Application
A systematic design method for reducing bias in observers is
developed. The method utilizes an observable default model of the
system together with measurement data from the real system and
estimates a model augmentation. The augmented model is then used to
design an observer which reduces the estimation bias compared to an
observer based on the default model. Three main results are a
characterization of possible augmentations from observability
perspectives, a parameterization of the augmentations from the
method, and a robustness analysis of the proposed augmentation
estimation method. The method is applied to a truck engine where the
resulting augmented observer reduces the estimation bias by 50%
in a European Transient Cycle.
Erik Höckerdal, Erik Frisk and Lars Eriksson
Control Engineering Practice,
2009
Informationsansvarig: webmaster
Senast uppdaterad: 2021-11-10
LiU Homepage
