Parameter Identification to Enforce Practical Observability of Nonlinear Systems

The sensitivity of the unmeasured state variables to the measurements strongly affects the rate of convergence of a state estimation algorithm. To overcome potential observability problems, the approach has been to identify the model parameters so as to reach a compromise between model accuracy and system observability. A cost function has been proposed that uses repeated optimization to select a coefficient that weighs the relative importance of these two objectives. This paper proposes a cost function that is the product of measures of these two objectives, thus alleviating the need for the trial-and-error selection of a weighting coefficient. The proposed identification procedure is evaluated with both simulated and experimental data, and with different observer structures.

Published in:
10th Computer Applications in Biotechnology
Presented at:
10th Computer Applications in Biotechnology, Cancun, Mexico, June 6-8, 2007

Note: The status of this file is: EPFL only

 Record created 2007-01-09, last modified 2018-01-27

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