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Abstract
In many applications, modeling of the plant for control purposes is a complex and time-consuming task in itself. Data-based approaches offer an alternative where a set of data collected from the plant are used as a means for direct controller selection. Virtual Reference Feedback Tuning (VRFT) is a data-based model-reference tuning technique that can be implemented at low experimental and computational cost, which has shown versatility and good performance in a variety of applications. VRFT comes accompanied by a mathematical theory that sets its quality properties: when the controller class is rich enough to secure perfect model-reference matching, VRFT is shown to deliver the optimal controller, while approximate optimality is obtained for reduced order controller design by pre-filtering techniques. In this talk, we mean to provide a general and easy-to-access overview of the VRFT method.
Biography
Marco Claudio Campi is professor of Automatic Control at the University of Brescia, Italy. He is the chair of the Technical Committee IFAC on Modeling, Identification and Signal Processing (MISP) and has been in various capacities on the Editorial Board of Automatica, Systems and Control Letters and the European Journal of Control. He is a recipient of the "Giorgio Quazza" prize, and, in 2008, received the IEEE CSS George S. Axelby outstanding paper award for the article "The Scenario Approach to Robust Control Design". He has delivered plenary and semi-plenary addresses at major conferences including SYSID, MTNS, and CDC. Currently he is a dinsinguished lecturer of the Control Systems Society. Marco Campi is a Fellow of IEEE, a member of IFAC, and a member of SIDRA.
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