Abstract In order to estimate parameters accurately in nonlinear dynamic systems, experiments that yield a maximum of information are invaluable. Such experiments can be obtained by exploiting model-based optimal experiment design techniques, which use the current guess for the parameters. This guess can differ from the actual system. Consequently, the experiment can result in a lower information content than expected and constraints are potentially violated. In this paper an efficient approach for stochastic optimal experiment design is exploited based on polynomial chaos expansion. This stochastic approach is compared with a sensitivities based approximate robust approach which aims to exploit (higher order) derivative information. Both approaches aim at a more conservative experiment design with respect to the information content and constraint violation. Based on two simulation case studies, practical guidelines are provided on which approach is best suited for robustness with respect to information content and robustness with respect to state constraints.

中文翻译：

---

参数不确定性下的优化实验设计：基于灵敏度的方法与基于多项式混沌的随机方法的比较

摘要 为了在非线性动态系统中准确估计参数，产生最大信息量的实验是非常宝贵的。此类实验可以通过利用基于模型的优化实验设计技术来获得，该技术使用参数的当前猜测。这种猜测可能与实际系统不同。因此，实验可能导致比预期更低的信息内容，并且可能违反约束。在本文中，一种基于多项式混沌展开的随机优化实验设计的有效方法被利用。这种随机方法与旨在利用（高阶）导数信息的基于灵敏度的近似鲁棒方法进行比较。这两种方法都针对信息内容和约束违规进行了更保守的实验设计。基于两个模拟案例研究，提供了实用指南，说明哪种方法最适合信息内容方面的鲁棒性和状态约束方面的鲁棒性。