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Abstract



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

2011

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