  We study approaches to robust model-based design of experiments in the
context of maximum-likelihood estimation. These approaches provide
robustification of model-based methodologies for the design of optimal
experiments by accounting for the effect of the parametric uncertainty. We
study the problem of robust optimal design of experiments in the framework of
nonlinear least-squares parameter estimation using linearized confidence
regions. We investigate several well-known robustification frameworks in this
respect and propose a novel methodology based on multi-stage robust
optimization. The proposed methodology aims at problems, where the experiments
are designed sequentially with a possibility of re-estimation in-between the
experiments. The multi-stage formalism aids in identifying experiments that are
better conducted in the early phase of experimentation, where parameter
knowledge is poor. We demonstrate the findings and effectiveness of the
proposed methodology using four case studies of varying complexity.
