Data-Driven Modeling of a Controlled Orthotropic Plate Using Machine Learning

2026/03/31 by Yongho Kim, Alexander Zuyev, Francesco Pellicano +2
Mathematics · #math.OC #msc:68T05 #msc:93B15

paper · pdf · doi:10.1109/med70602.2026.11598422

published as 2026 34th Mediterranean Conference on Control and Automation (MED), pp. 467-472 · This is a preprint version of the manuscript accepted to appear in the proceedings of the 34th Mediterranean Conference on Control and Automation (MED 2026)

arxiv created 2026/06/03 · openalex publication_date 2026/06/23 · openalex created_date 2026/07/17 · openalex updated_date 2026/07/17 · arxiv updated 2026/07/30

Abstract

We study the problem of learning the input-output map of a controlled vibrating plate with a composite structure from experimental measurements. Analytical modeling of this control system faces challenges due to the essential orthotropy and unknown damping characteristics of the material. Surrogate models based on linear regression, multilayer perceptrons, and gated recurrent units are constructed from the available sampled data. Through comparative analysis, we show that the multilayer perceptron model provides an acceptable approximation of this dynamical system, capturing the potentially nonlinear phenomena in its input-output behavior.

Citations

• A Data-Driven, Energy-based Approach for Identifying Equations of Motion in Vibrating Structures Directly from Measurements
• Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

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