Set-membership error-in-variables identification through convex relaxation techniques

V. Cerone, D. Piga, D. Regruto

Research output: Contribution to journalArticleAcademicpeer-review

48 Citations (Scopus)

Abstract

In this technical note, the set membership error-in-variables identification problem is considered, that is the identification of linear dynamic systems when both output and input measurements are corrupted by bounded noise. A new approach for the computation of parameter uncertainty intervals is presented. First, the identification problem is formulated in terms of nonconvex optimization. Then, relaxation techniques based on linear matrix inequalities are employed to evaluate parameter bounds by means of convex optimization. The inherent structured sparsity of the original identification problems is exploited to reduce the computational complexity of the relaxed problems. Finally, convergence properties and complexity of the proposed procedure are discussed. Advantages of the presented technique with respect to previously published results are discussed and shown by means of two simulated examples.
Original languageEnglish
Pages (from-to)517-522
Number of pages6
JournalIEEE Transactions on Automatic Control
Volume57
Issue number2
DOIs
Publication statusPublished - 2012

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