A novel convex state-space solution to the robust output-feedback synthesis problem based on dynamic integral quadratic constraints for a particular class of systems is
presented. Convexification rests upon the assumption that the control-to-uncertainty block in the generalized plant commutes with the off-diagonal block of the multiplier. We demonstrate that this commutation property is valid for several by themselves interesting concrete scenarios, such as in extremum control, a generalization of the convex design of first-order optimization algorithms involving filtered gradient evaluations.
%0 Conference Paper
%1 noauthororeditor
%A Holicki, T.
%A Scherer, C. W.
%B Proc. 60th IEEE Conf. Decision and Control
%D 2021
%K myown from:tobiasholicki pn4 EXC2075 peerreviewed imng
%P 3249-3256
%R 10.1109/CDC45484.2021.9683012
%T Algorithm Design and Extremum Control: Convex Synthesis due to Plant Multiplier Commutation
%U https://doi.org/10.1109/CDC45484.2021.9683012
%X A novel convex state-space solution to the robust output-feedback synthesis problem based on dynamic integral quadratic constraints for a particular class of systems is
presented. Convexification rests upon the assumption that the control-to-uncertainty block in the generalized plant commutes with the off-diagonal block of the multiplier. We demonstrate that this commutation property is valid for several by themselves interesting concrete scenarios, such as in extremum control, a generalization of the convex design of first-order optimization algorithms involving filtered gradient evaluations.
@inproceedings{noauthororeditor,
abstract = {A novel convex state-space solution to the robust output-feedback synthesis problem based on dynamic integral quadratic constraints for a particular class of systems is
presented. Convexification rests upon the assumption that the control-to-uncertainty block in the generalized plant commutes with the off-diagonal block of the multiplier. We demonstrate that this commutation property is valid for several by themselves interesting concrete scenarios, such as in extremum control, a generalization of the convex design of first-order optimization algorithms involving filtered gradient evaluations.},
added-at = {2022-02-09T20:15:52.000+0100},
author = {Holicki, T. and Scherer, C. W.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/292c1e04c8d8147cb735f9a491ec90bc0/mathematik},
booktitle = {Proc. 60th IEEE Conf. Decision and Control},
doi = {10.1109/CDC45484.2021.9683012},
interhash = {0d4bcef5834e1ce865f708187791380d},
intrahash = {92c1e04c8d8147cb735f9a491ec90bc0},
keywords = {myown from:tobiasholicki pn4 EXC2075 peerreviewed imng},
pages = {3249-3256},
timestamp = {2023-10-31T14:48:40.000+0100},
title = {Algorithm Design and Extremum Control: Convex Synthesis due to Plant Multiplier Commutation},
url = {https://doi.org/10.1109/CDC45484.2021.9683012},
year = 2021
}
