<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimizing the convergence of data-based controller tuning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">226</style></volume><pages><style face="normal" font="default" size="100%">563–574</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Data-based control design methods most often consist of iterative adjustment of the controller?s parameters towards the parameter values which minimize an Formula performance criterion. Typically, batches of input-output data collected from the system are used to feed directly a gradient descent optimization algorithm ? no process model is used. Two topics are important regarding this algorithm: the convergence rate and the convergence to the global minimum. This paper discusses these issues and provides a method for choosing the step size to ensure convergence with high convergence rate, as well as a test to verify at each step whether or not the algorithm is converging to the global minimum.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes></record></records></xml>