Complexity reduction for uncertain systems:a projection-based approach

This paper considers the problem of complexity reduction for systems with affine parametric uncertainty. We are interested in the relation between model reduction for a nominal plant and dimension reduction for a parameter vector. By using linear fractional representations of the system, it is shown that a projection-based reduction approach can be applied separately to the generalized plant and the uncertainty block. The error bounds between the original system and its reduced order approximation are derived, and a case study is used to validate our findings.