Benchmarking of Flatness-based Control of the Heat Equation

Flatness-based control design is a well established method to generate open-loop control signals. Several articles discuss the application of flatness-based control design of (reaction-) diffusion problems for various scenarios. Beside the pure analytical derivation also the numerical computation of the input signal is crucial to yield a reliable trajectory planning. Therefore, we derive the input signal step-by-step and describe the influence of system and controller parameters on the computation of the input signal. In particular, we benchmark the control design of the one-dimensional heat equation with Neumann-type boundary actuation for pure aluminum and steel 38Si7, and discuss the applicability of the found input signals for realistic scenarios.

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Publication Details

Journal
SNE Simulation Notes Europe
Published
2026-05-31
DOI
https://doi.org/10.11128/sne.35.tn.10773
Primary Topic
Model Reduction and Neural Networks
Type
preprint
Field-Weighted Citation Impact
0.00
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preprint

Benchmarking of Flatness-based Control of the Heat Equation

Dirk Lebiedz, Lothar Berger, S. Scholz
SNE Simulation Notes Europe
Model Reduction and Neural Networks
preprint

Benchmarking of Flatness-based Control of the Heat Equation

Dirk Lebiedz, Lothar Berger, S. Scholz
preprint en

Abstract

Flatness-based control design is a well established method to generate open-loop control signals. Several articles discuss the application of flatness-based control design of (reaction-) diffusion problems for various scenarios. Beside the pure analytical derivation also the numerical computation of the input signal is crucial to yield a reliable trajectory planning. Therefore, we derive the input signal step-by-step and describe the influence of system and controller parameters on the computation of the input signal. In particular, we benchmark the control design of the one-dimensional heat equation with Neumann-type boundary actuation for pure aluminum and steel 38Si7, and discuss the applicability of the found input signals for realistic scenarios.

SNE Simulation Notes EuropeVol. 36(2)
Sustainable cities and communities
Openalex Percentile: Top 100%
Model Reduction and Neural Networks
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