Model validation for dynamically uncertain systems

Robust control models describe system uncertainty with both unknown additive signals and unknown dynamic perturbations. These unknown but bounded components lead to a model set description. Model validation is the experimental assessment of the ability of this model set to describe the observed system behaviors. In this paper we consider model validation for H_∞ compatible models. This paper provides a detailed presentation of the H_∞ model validation problem in the discrete frequency, discrete-time, and sampled-data frameworks. In each case the underlying results and the computational algorithms are discussed. The experimental applicability and the computational consequences are discussed in sufficient detail to give the reader an appreciation of the issues surrounding each model/experiment framework.