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Model reduction techniques with a-posteriori error analysis for linear-quadratic optimal control problems

Model reduction techniques with a-posteriori error analysis for linear-quadratic optimal control problems

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Prüfsumme: MD5:340aec36a28b7a381a56f9965d375c15

VOSSEN, Georg, Stefan VOLKWEIN, 2012. Model reduction techniques with a-posteriori error analysis for linear-quadratic optimal control problems

@unpublished{Vossen2012Model-18465, title={Model reduction techniques with a-posteriori error analysis for linear-quadratic optimal control problems}, year={2012}, author={Vossen, Georg and Volkwein, Stefan} }

Volkwein, Stefan The main focus of this paper is on an a-posteriori analysis for different model-order strategies applied to optimal control problems governed by linear parabolic partial differential equations. Based on a perturbation method it is deduced how far the suboptimal control, computed on the basis of the reduced-order model, is from the (unknown) exact one. For the model-order reduction, H_(2,alpha)-norm optimal model reduction (H2), balanced truncation (BT), and proper orthogonal decomposition (POD) are studied.<br /><br />The proposed approach is based on semi-discretization of the underlying dynamics for the state and the adjoint equations as a large scale linear time-invariant (LTI) system. This system is reduced to a lower-dimensional one using Galerkin (POD) or Petrov-Galerkin (H2, BT) projection. The size of the reduced-order system is iteratively increased until the error in the optimal control, computed with the a-posteriori error estimator, satisfies<br />a given accuracy. The method is illustrated with numerical tests. eng Vossen, Georg Model reduction techniques with a-posteriori error analysis for linear-quadratic optimal control problems deposit-license Vossen, Georg 2012-02-29T12:49:04Z 2012-02-29T12:49:04Z 2012 Volkwein, Stefan

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

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