Starting from a linear fractional representation of a linear system affected by constant parametric uncertainties, we demonstrate how to enhance standard robust analysis tests by taking available (noisy) input-output data of the uncertain system into account. Our approach relies on a lifting of the system and on the construction of data-dependent multipliers. It leads to a test in terms of linear matrix inequalities which guarantees stability and performance for all systems compatible with the observed data if it is in the affirmative. In contrast to many other data-based approaches, prior physical knowledge is included at the outset due to the underlying linear fractional representation.
%0 Generic
%1 holicki2023inputoutputdataenhanced
%A Holicki, Tobias
%A Scherer, Carsten W.
%D 2023
%K from:tobiasholicki pn4 prePrint EXC2075 imng
%R 10.48550/arXiv.2211.02149
%T Input-Output-Data-Enhanced Robust Analysis via Lifting
%X Starting from a linear fractional representation of a linear system affected by constant parametric uncertainties, we demonstrate how to enhance standard robust analysis tests by taking available (noisy) input-output data of the uncertain system into account. Our approach relies on a lifting of the system and on the construction of data-dependent multipliers. It leads to a test in terms of linear matrix inequalities which guarantees stability and performance for all systems compatible with the observed data if it is in the affirmative. In contrast to many other data-based approaches, prior physical knowledge is included at the outset due to the underlying linear fractional representation.
@misc{holicki2023inputoutputdataenhanced,
abstract = {Starting from a linear fractional representation of a linear system affected by constant parametric uncertainties, we demonstrate how to enhance standard robust analysis tests by taking available (noisy) input-output data of the uncertain system into account. Our approach relies on a lifting of the system and on the construction of data-dependent multipliers. It leads to a test in terms of linear matrix inequalities which guarantees stability and performance for all systems compatible with the observed data if it is in the affirmative. In contrast to many other data-based approaches, prior physical knowledge is included at the outset due to the underlying linear fractional representation.},
added-at = {2023-02-25T15:05:26.000+0100},
author = {Holicki, Tobias and Scherer, Carsten W.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2b49b0b3c09137006eb4a9727a904dcf0/mst},
doi = {10.48550/arXiv.2211.02149},
interhash = {7f039cad88696b9e0f60fbdd2b61c8a7},
intrahash = {b49b0b3c09137006eb4a9727a904dcf0},
keywords = {from:tobiasholicki pn4 prePrint EXC2075 imng},
timestamp = {2024-03-12T10:23:40.000+0100},
title = {Input-Output-Data-Enhanced Robust Analysis via Lifting},
year = 2023
}
