Identification for control: optimal input design with respect to a worst-case ν-gap cost function

The aim of this contribution is to demonstrate efficient applicability of modern convex optimization techniques in control theory. We solve the problem of designing an input for a parameter identification experiment such that the worst-case ν-gap over all plants in the resulting uncertainty region between the identified plant and plants in this region is as small as possible. The motivation for choosing this cost criterion is robust controller design, where the controller has to stabilize all plants in the identified uncertainty region.