Nonlinear functional characterizations of uncertainty in model validation

Model validation provides a useful means of assessing the ability of a model to account for a specific experimental observation, and has application to modeling, identifi- cation and fault detection. In this paper we consider a new approach to the linear fractional transformation (LFT) model validation problem by deploying quadratic functionals, and more generally nonlinear functionals, to specify noise and dynamical perturbation sets. Sufficient conditions for invalidation of such models are provided in terms of semidefinite programming problems.ISSN:1474-667