Constrained Optimal Synthesis and Robustness Analysis by Randomized Algorithms

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dc.creatorChen, Xinjia
dc.creatorZhou, Kemin
dc.date2008-05-12
dc.date.accessioned2026-07-07T09:38:18Z
dc.date.available2026-07-07T09:38:18Z
dc.descriptionIn this paper, we consider robust control using randomized algorithms. We extend the existing order statistics distribution theory to the general case in which the distribution of population is not assumed to be continuous and the order statistics is associated with certain constraints. In particular, we derive an inequality on distribution for related order statistics. Moreover, we also propose two different approaches in searching reliable solutions to the robust analysis and optimal synthesis problems under constraints. Furthermore, minimum computational effort is investigated and bounds for sample size are derived.
dc.description14 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/0805.1571
dc.identifierhttp://arxiv.org/abs/0805.1571
dc.identifierIEEE Transactions on Automatic Control, vol. 45, pp. 1180-1186, June 2000
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/160758
dc.subjectOptimization and Control
dc.subjectStatistics Theory
dc.titleConstrained Optimal Synthesis and Robustness Analysis by Randomized Algorithms
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