Robust multi-stage model-based design of optimal experiments for nonlinear estimation

We study approaches to the 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 frame-works 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 possi-bility of re-estimation in-between the experiments. The multi-stage formalism aids in identifying exper-iments 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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The study focuses on robust model-based design of experiments in the context of maximum-likelihood estimation, utilizing a multi-stage robust optimization approach to address parametric uncertainties in experiment design. The findings suggest that conducting experiments in multiple stages can improve effectiveness when parameter knowledge is limited.