Abstract
Robust control techniques require the construction of uncertainty model sets. When dealing with unstructured norm-bounded uncertainties, it is important that the size of the uncertainty set is minimized, so that robust performances can be enhanced. This paper addresses the problem of constructing the minimum l 1 uncertainty model set containing a finite set of assigned models. The problem is formulated as a conditional Chebyshev center problem and an efficient algorithm for its solution is proposed. The algorithm converges in a finite number of steps and is able to deal with large size problems in reasonable time.
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