Paganini, Fernando (1995) A Set-Based Methodology for White Noise Modeling. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechCDSTR:1995.023
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This paper provides a new framework for analyzing white noise disturbances in linear systems: rather than the usual stochastic approach, noise signals are described as elements in sets and their effect is analyzed from a worst-case perspective. The paper studies how these sets must be chosen in order to have adequate properties for system response in the worst-case, statistics consistent with the stochastic point of view, and simple descriptions that allow for tractable worst-case analysis. The methodology is demonstrated by considering its implications in two problems: rejection of white noise signals in the presence of system uncertainty, and worst-case system identification.
|Item Type:||Report or Paper (Technical Report)|
|Additional Information:||The author would like to thank John Doyle for motivation and helpful discussions at Caltech, Geir Dullerud for useful suggestions which helped simplify the proofs, and Stefano Soatto, Giorgio Picci and Adelchi Azzalini for useful references. This work was supported by AFOSR, NSF and by the Universidad de la Republica, Uruguay.|
|Group:||Control and Dynamical Systems Technical Reports|
|Usage Policy:||You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.|
|Deposited By:||Imported from CaltechCDSTR|
|Deposited On:||16 Oct 2006|
|Last Modified:||26 Dec 2012 14:30|
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