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Article overview
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Risk Analysis in Robust Control -- Making the Case for Probabilistic
Robust Control | | Xinjia Chen
; Jorge Aravena
; Kemin Zhou
; | | Date: |
5 Jul 2007 | | Abstract: | This paper offers a critical view of the "worst-case" approach that is the
cornerstone of robust control design. It is our contention that a blind
acceptance of worst-case scenarios may lead to designs that are actually more
dangerous than designs based on probabilistic techniques with a built-in risk
factor. The real issue is one of modeling. If one accepts that no mathematical
model of uncertainties is perfect then a probabilistic approach can lead to
more reliable control even if it cannot guarantee stability for all possible
cases. Our presentation is based on case analysis. We first establish that
worst-case is not necessarily "all-encompassing." In fact, we show that for
some uncertain control problems to have a conventional robust control solution
it is necessary to make assumptions that leave out some feasible cases. Once we
establish that point, we argue that it is not uncommon for the risk of
unaccounted cases in worst-case design to be greater than that of the accepted
risk in a probabilistic approach. With an example, we quantify the risks and
show that worst-case can be significantly more risky. Finally, we join our
analysis with existing results on computational complexity and probabilistic
robustness to argue that the deterministic worst-case analysis is not
necessarily the better tool. | | Source: | arXiv, arxiv.0707.0878 | | Services: | Forum | Review | PDF | Favorites | |
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