Parameter Identification to Enforce Practical Observability of Nonlinear Systems

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

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.

Keywords: Parameter identification ; Observability ; State estimation ; Identification for estimation ; Kalman filter ; Particle filter

Reference

• LA-CONF-2007-001

• URL: http://www.cab2007.org/

Record created on 2007-01-09, modified on 2012-03-21