Towards a Methodology for Robust Parameter Identification

Abstract

The paper considers the problem of estimating, from experimental data, real parameters for a model with uncertainty in the form of both additive noise and norm bounded perturbations. Such models frequently arise in robust control theory, and a framework is introduced for the consideration of experimental data in robust control analysis problems. If the analysis tools applied include robust stability tests for real parameter variations (real μ), then the framework can be used to address the problem of "robust" parameter identification. While the techniques discussed here can quickly become computationally overwhelming when applied to physical systems and real data, the approach introduces a new way of looking at the identification problem and may be helpful in arriving at a more tractable methodology.