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Globally Convergent Algorithms for Robust Pole Assignment by State Feedback

dc.contributor.authorTits, A.L.en_US
dc.contributor.authorYang, Y.en_US
dc.date.accessioned2007-05-23T09:58:24Z
dc.date.available2007-05-23T09:58:24Z
dc.date.issued1995en_US
dc.identifier.urihttp://hdl.handle.net/1903/5598
dc.description.abstractIt is observed that an algorithm proposed in 1985 by Kautsky, Nichols and Van Dooren (KNV) amounts to maximize, at each iteration, the determinant of the candidate closed-loop eigenvector matrix X with respect to one of its columns (with unit length constraint), subject to the constraint that it remains a valid closed-loop eigenvector matrix. This interpretation is used to prove convergence of the KNV algorithm. It is then shown that a more efficient algorithm is obtained if det (X) is concurrently maximized with respect to two columns of X, and that such a scheme is easily extended to the case where the eigenvalues to be assigned include complex conjugate pairs. Variations exploiting the availability of multiple processors are suggested. Convergence properties of the proposed algorithms are established. Their superiority is demonstrated numerically.en_US
dc.format.extent1409137 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1995-3en_US
dc.subjectalgorithmsen_US
dc.subjectcomputer aided designen_US
dc.subjectlinear systemsen_US
dc.subjectoptimizationen_US
dc.subjectrobust controlen_US
dc.subjectIntelligent Control Systemsen_US
dc.titleGlobally Convergent Algorithms for Robust Pole Assignment by State Feedbacken_US
dc.typeTechnical Reporten_US
dc.contributor.departmentISRen_US


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