Data from Numerical Continuation in Nonlinear Experiments using Local Gaussian Process Regression

Control-based continuation is a new methodology that allows experimenters to systematically investigate the dynamic behaviour of laboratory-based nonlinear systems, whether they are engineered devices or biological systems. The focus is on finding boundaries between qualitatively different types of behaviour; these boundaries (so-called bifurcations) are then automatically tracked as system parameters are varied, using a range of ideas from control theory, dynamical systems and numerical analysis.

The success of this proposal will enable the widespread uptake of control-based continuation, across engineering and the applied sciences, thus greatly easing the difficulties of experimentally characterising nonlinear systems. Three key objectives will be addressed: 1) estimation of the local linearisation of a steady-state directly from the controlled experiment; 2) making the underlying numerical methods fast and robust to experimental noise; and 3) demonstrating the methodology on a multi-degree-of-freedom system.

Creator(s) Ludovic Renson
Contributor(s) Ludovic Renson
Publication date 05 Aug 2019
Language eng
Publisher University of Bristol
Licence Creative Commons Attribution 4.0
Warranty Disclaimer of Warranties and Limitation of Liability
DOI 10.5523/bris.1ttza2mrigjbt2mmbp6raqrxen
Complete download (zip) https://data.bris.ac.uk/datasets/tar/1ttza2mrigjbt2mmbp6raqrxen.zip
Citation Ludovic Renson (2019): Data from Numerical Continuation in Nonlinear Experiments using Local Gaussian Process Regression. https://doi.org/10.5523/bris.1ttza2mrigjbt2mmbp6raqrxen
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