Worst-case performance analysis with constrained uncertainty

Pete Seiler, Andrew K. Packard

Research output: Contribution to journalConference articlepeer-review


In this paper, we examine the worst-case performance of a linear system with real parametric uncertainty. In particular, we will analyze the worst-case gain from disturbances to errors of a system subjected to 2 real, scalar uncertainties. The 2 scalar uncertainties are typically normalized so that they have absolute value less than or equal to one. In the parameter space, this constrains the uncertainties to lie in the unit cube. The contribution of this paper is that we also assume that the 2 scalar parameters are correlated. This correlation is represented by an additional offset rectangle constraint in the parameter space. The motivation for this problem is to use our knowledge of parameter correlation to remove some of the conservativeness in the standard performance analysis.

Original languageEnglish (US)
Pages (from-to)1107-1112
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 2001
Event40th IEEE Conference on Decision and Control (CDC) - Orlando, FL, United States
Duration: Dec 4 2001Dec 7 2001


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