<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">L. Campestrini</style></author><author><style face="normal" font="default" size="100%">D. Eckhard</style></author><author><style face="normal" font="default" size="100%">A. S. Bazanella</style></author><author><style face="normal" font="default" size="100%">M. Gevers</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model Reference Control Design by Prediction Error Identification</style></title><secondary-title><style face="normal" font="default" size="100%">16th {IFAC} Symposium on System Identification</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">IFAC</style></publisher><pub-location><style face="normal" font="default" size="100%">Brussels</style></pub-location><pages><style face="normal" font="default" size="100%">1478–1483</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper studies a one-shot (non-iterative) data-based method for Model Reference (MR) control design. It shows that the optimal controller can be obtained as the solution of a Prediction Error (PE) identification problem that directly estimates the controller parameters through a reparametrization of the input-output model. The standard tools of PE Identification can thus be used to analyze the statistical properties (bias and variance) of the estimated controller. It also shows that, for MR control design, direct and indirect data-based methods are essentially equivalent.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes></record></records></xml>