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.


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    Title :

    Observer design and model augmentation for bias compensation with a truck engine application


    Additional title:

    Beobachterentwurf und Modellerweiterung für die Fehlerkompensation mit Anwendung bei einem Nutzfahrzeugmotor


    Contributors:

    Published in:

    Publication date :

    2009


    Size :

    10 Seiten




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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