An approach to model validation in the μ framework

The problem of model validation of postulated system models when experimental data is available is addressed in this paper. The approach presented requires a μ test across frequency and shows that the structure of the model set in terms of the size and type of uncertainty and noise weights plays a central role in the model validation process. In general, increasing the size of the model set increases the ease of validation of a particular model. The proposed model-validation technique is used to synthesize controllers for an experimental structure which significantly out performs poorly selected uncertainty weights.