The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Iterative Feedback Tuning (IFT) is a direct tuning method using closed loop experimental data. The method is based on numerical optimization and in each iteration an unbiased gradient estimate is used. In this contribution we show how to use IFT to do robust loopshaping. One method, based on the approach suggested by Glover and McFarlane [1], uses an approximate H∞ cost function and alternates between...
In this paper, a general sparse estimator is proposed, based on the maximum likelihood / prediction error method (or any √N-consistent estimator). This procedure does not rely on the convexity of the cost function of the underlying estimator (in case such estimator is an M-estimator), and it provides an automatic tuning of the (implicit) regularization parameter. The idea behind the proposed method...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.