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arXiv · 2608.13360

Robust Space-Filling Input Design via Stochastic Optimization

Máté Kiss·Roland Tóth·Maarten Schoukens
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Abstract

The space-filling input design approach generates a so-called space-filling dataset in the feature space of the system model. The design method is applicable on a broad class of model structures with wide selection of signals and also incorporates information measures through optimality criteria into the signal design. However, during the signal design, knowledge of a hypothesized model is required. The designed signal can perform far from the optimal if the true system is significantly different from the hypothesized system model. This paper proposes a robust space-filling input design algorithm that can generate a space-filling dataset for an entire class of models. The proposed algorithm takes the expectation of an optimality criterion over the population of the model class, and a stochastic approximation technique is employed to optimize this robust criteria. The efficiency of the proposed algorithm is demonstrated in a simulation example.

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Máté Kiss, Roland Tóth, Maarten Schoukens. 2026-08-13. Robust Space-Filling Input Design via Stochastic Optimization. https://arxiv.org/abs/2608.13360

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