A risk-adjusted approach to model (in)validation

This paper presents a risk-adjusted approach to the problem of model (in)validation of LTI systems subject to structured dynamic uncertainty entering the model in LFT form. The proposed method proceeds by sampling the set of admissible uncertainties, with the aim of finding at least one element that together with the candidate model can reproduce the experimental data. If so, the model is not invalidated by experimental evidence. Otherwise, if no such element exists, the model is invalidated by the data with a certain probability. As we show in the paper, give ε >0, it is possible to determine a priori the number of samples so that the probability a valid model is below ε. Thus, by introducing a relaxation in terms of this risk ε, we can overcome the computational complexity associated with model invalidation in the presence of structured uncertainties.