PARAMETER IDENTIFICATION TO ENFORCE PRACTICAL OBSERVABILITY OF NONLINEAR SYSTEMS

The sensitivity of measurements to unmeasured state variables strongly affects the rate of convergence of a state estimator. 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. An objective function that weighs the relative importance of these two objectives has been proposed in the literature. However, this scheme relies on an extensive heuristic search to select the weighting coefficients. This paper proposes an objective function that is the product of measures of these two objectives, thus alleviating the need for the trial-and-error selection of the weighting coefficient. The proposed identification procedure is evaluated using both simulated and experimental data, and with different observer structures.