000111775 001__ 111775
000111775 005__ 20190316234031.0
000111775 02470 $$2ISI$$a000255181700181
000111775 037__ $$aCONF
000111775 245__ $$aData-Driven Estimation of the Infinity Norm of a Dynamical System
000111775 260__ $$c2007
000111775 269__ $$a2007
000111775 336__ $$aConference Papers
000111775 520__ $$aThe estimation of a system’s infinity norm using one set of measured input and output data is investigated. It is known that, if the data set is noise free, this problem can be solved using convex optimization. In the presence of noise, convergence of this estimate to the true infinity norm of the system is no longer guaranteed. In this paper, a convex noise set is defined in the time domain using decorrelation between the noise and the system input. For infinite data length, we prove that the estimate of the infinity norm converges to its true value. A simulation example shows the behavior for finite data length. In addition, the method is used to test closed-loop stability in the context of data-driven controller tuning. A sufficient condition for stability in terms of an infinity norm is introduced. The effectiveness of the proposed stability test is illustrated via a simulation example.
000111775 6531_ $$aData-Driven
000111775 6531_ $$aIdentification
000111775 6531_ $$aCorrelation approach
000111775 6531_ $$aConvex noise set
000111775 700__ $$aVan Heusden, Klaske
000111775 700__ $$0240447$$aKarimi, Alireza$$g139973
000111775 700__ $$0240449$$aBonvin, Dominique$$g104596
000111775 7112_ $$aIEEE Conference on Decision and Control$$cNew Orleans$$dDecember 12-14, 2007
000111775 773__ $$t46th IEEE Conference on Decision and Control
000111775 8564_ $$zURL
000111775 8564_ $$s387317$$uhttps://infoscience.epfl.ch/record/111775/files/CDC_2007_vanHeusden.pdf$$zn/a
000111775 909C0 $$0252053$$pLA
000111775 909CO $$ooai:infoscience.tind.io:111775$$pSTI$$pconf$$qGLOBAL_SET
000111775 937__ $$aLA-CONF-2007-006
000111775 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000111775 980__ $$aCONF