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Soft vs. hard bounds in probabilistic robustness analysis

Zhu, Xiaoyun and Huang, Yun and Doyle, John (1996) Soft vs. hard bounds in probabilistic robustness analysis. In: Proceedings of 35th IEEE Conference on Decision and Control. Vol.3. IEEE , Piscataway, NJ, pp. 3412-3417. ISBN 0-7803-3590-2.

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The relationship between soft vs. hard bounds and probabilistic vs. worst-case problem formulations for robustness analysis has been a source of some apparent confusion in the control community, and this paper will attempt to clarify some of these issues. Essentially, worst-case analysis involves computing the maximum of a function which measures performance over some set of uncertainty. Probabilistic analysis assumes some distribution on the uncertainty and computes the resulting probability measure on performance. Exact computation in each case is intractable in general, and this paper explores the use of both soft, and hard bounds for computing estimates of performance, including extensive numerical experimentation. We will focus on the simplest possible problem formulations that we believe reveal the difficulties associated with more general robustness analysis.

Item Type:Book Section
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Doyle, John0000-0002-1828-2486
Additional Information:© 1996 IEEE.
Record Number:CaltechAUTHORS:20190319-103641203
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Official Citation:Xiaoyun Zhu, Yun Huang and J. Doyle, "Soft vs. hard bounds in probabilistic robustness analysis," Proceedings of 35th IEEE Conference on Decision and Control, Kobe, Japan, 1996, pp. 3412-3417 vol.3. doi: 10.1109/CDC.1996.573688
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93962
Deposited By: Tony Diaz
Deposited On:19 Mar 2019 18:14
Last Modified:16 Nov 2021 17:01

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