Paper
Data-Driven Estimation of Vinnicombe metric
Authors
Margarita A. Guerrero, Henrik Sandberg, Cristian R. Rojas
Abstract
Quantifying model mismatch in a control-relevant manner is fundamental in robust control. A well-known metric for this purpose is the $ν$-gap, or Vinnicombe metric, which measures the discrepancy between a nominal model and the real system from a closed-loop viewpoint. However, its computation typically requires explicit knowledge of the true system. In this letter, we propose an identification-free, data-driven method to estimate the $ν$-gap between discrete-time SISO systems directly from input-output experiments. The method is complemented by a data-driven winding-number test, based on Welch-type averaging, to verify a required topological condition for the computation of the metric. Numerical simulations on heavy-duty gas-turbine models and a textbook example show that the proposed estimate closely matches MATLAB$^©$ \texttt{gapmetric}, while correctly detecting cases in which the admissibility conditions fail.
Metadata
Related papers
Fractal universe and quantum gravity made simple
Fabio Briscese, Gianluca Calcagni • 2026-03-25
POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan
Marta Moscati, Muhammad Saad Saeed, Marina Zanoni, Mubashir Noman, Rohan Kuma... • 2026-03-25
LensWalk: Agentic Video Understanding by Planning How You See in Videos
Keliang Li, Yansong Li, Hongze Shen, Mengdi Liu, Hong Chang, Shiguang Shan • 2026-03-25
Orientation Reconstruction of Proteins using Coulomb Explosions
Tomas André, Alfredo Bellisario, Nicusor Timneanu, Carl Caleman • 2026-03-25
The role of spatial context and multitask learning in the detection of organic and conventional farming systems based on Sentinel-2 time series
Jan Hemmerling, Marcel Schwieder, Philippe Rufin, Leon-Friedrich Thomas, Mire... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.17545v1</id>\n <title>Data-Driven Estimation of Vinnicombe metric</title>\n <updated>2026-03-18T09:49:54Z</updated>\n <link href='https://arxiv.org/abs/2603.17545v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.17545v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Quantifying model mismatch in a control-relevant manner is fundamental in robust control. A well-known metric for this purpose is the $ν$-gap, or Vinnicombe metric, which measures the discrepancy between a nominal model and the real system from a closed-loop viewpoint. However, its computation typically requires explicit knowledge of the true system. In this letter, we propose an identification-free, data-driven method to estimate the $ν$-gap between discrete-time SISO systems directly from input-output experiments. The method is complemented by a data-driven winding-number test, based on Welch-type averaging, to verify a required topological condition for the computation of the metric. Numerical simulations on heavy-duty gas-turbine models and a textbook example show that the proposed estimate closely matches MATLAB$^©$ \\texttt{gapmetric}, while correctly detecting cases in which the admissibility conditions fail.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='math.OC'/>\n <published>2026-03-18T09:49:54Z</published>\n <arxiv:comment>7 pages. Submitted to LCSS-CDC 2026</arxiv:comment>\n <arxiv:primary_category term='math.OC'/>\n <author>\n <name>Margarita A. Guerrero</name>\n </author>\n <author>\n <name>Henrik Sandberg</name>\n </author>\n <author>\n <name>Cristian R. Rojas</name>\n </author>\n </entry>"
}