Abstract
In this note, we address the problem of risk assessment when the robustness margin is exceeded, without a priori knowledge of the distribution of the uncertainty. The only assumption is that the distribution belongs to a given class. In contrast to previous work, this class contains both symmetric and nonsymmetric distributions. We prove that the assessment of risk can be done using only a subset of the admissible distributions. Also, if the set of uncertainties that verify the specifications is convex, it is proven that risk assessment can be done using only a finite subset of the class. Finally, a way of estimating risk is provided for the nonconvex case.
Original language | English (US) |
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Pages (from-to) | 1990-1994 |
Number of pages | 5 |
Journal | IEEE Transactions on Automatic Control |
Volume | 48 |
Issue number | 11 |
DOIs | |
State | Published - Nov 2003 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering