Abstract
The thesis that noisy identification has close ties to the study of the singular-value decomposition of perturbed matrices is investigated. In particular by assuming an upper bound on the norm of the perturbation, one can obtain a convex parametrization of an uncertain family of systems which contains the system generating the data. In this approach, the second-smallest singular value σ* of an appropriately defined data matrix becomes a quantity of importance as it provides an upper bound for the size of the uncertain family. This yields a new tool leading to the design of input functions which are optimal or persistently exciting from the point of view of identification for robust control.
Original language | English |
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Pages (from-to) | 1009-1031 |
Number of pages | 23 |
Journal | Automatica |
Volume | 35 |
Issue number | 6 |
DOIs | |
Publication status | Published - Jun 1999 |