Abstract
If the structure of the uncertainty in a linear model is known, it is natural to use this information in robustness analysis. In particular, when the model depends on a number of uncertain parameters one sometimes defines a “structured stability margin” measuring the smallest parameter deviation giving instability. There are different definitions of the structured stability margin. They differ in the way the structure of the uncertainty is prescribed. In this article we suggest a new definition that use the probabilistic distribution of the parameters. We will define and calculate a ‘structured stability margin’ which is tailor made to make use of covariance information on parametric uncertainty. Such information is typically obtained from a parametric identification.
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