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Application of Statistical Learning Control to the Design of a Fixed-Order Controller for a Flexible Beam

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/20439

Application of Statistical Learning Control to the Design of a Fixed-Order Controller for a Flexible Beam

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Title: Application of Statistical Learning Control to the Design of a Fixed-Order Controller for a Flexible Beam
Author: Abdallah, Chaouki T.; Ariola, M.; Koltchinskii, V.
Subject: Statistical Learning
Radamacher bootstrap
Robust Control
Sample Complexity
NP-hard problems
Decidability theory
Abstract: This paper shows how probabilistic methods and statistical learning theory can provide approximate solutions to “difficult” control problems. The paper also introduces bootstrap learning methods to drastically reduce the bound on the number of samples required to achieve a performance level. These results are then applied to obtain more efficient algorithms which probabilistically guarantee stability and robustness levels when designing controllers for uncertain systems. The paper includes examples of the applications of these methods.
Date: 2012-04-26
URI: http://hdl.handle.net/1928/20439


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