Validation of an Experimentally Derived Uncertainty Model

Abstract

The results show that uncertainty models can be obtained directly from system identification data by using a minimum norm model validation approach. The error between the test data and an analytical nominal model is modeled as a combination of unstructured additive and structured input multiplicative uncertainty. Robust controllers which use the experimentally derived uncertainty model show significant stability and performance improvements over controllers designed with assumed ad hoc uncertainty levels. Use of the identified uncertainty model also allowed a strong correlation between design predictions and experimental results.