Towards a state-space polytopic uncertainty description using subspace model identification techniques

In this paper we introduce a novel method for the estimation of the uncertainty on a model, identified using subspace identification methods. The key to this method is the calculation of the first-order approximation of the relation between the perturbation on the data and the error on the elements in the identified state-space matrices. Using the first-order approximation we can find a polytopic description of the uncertainty region of the identified model. This polytopic description can for instance be used to develop a robust(ified) controller for the plant.