|Time:||January 14, 2020, 4:00 p.m. (CET)|
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The Linear Parameter-Varying (LPV) framework has been introduced with the intention to provide stability and performance guarantees for analysis and controller synthesis for Nonlinear (NL) systems via convex methods. By extending results of the LTI framework, it was assumed that they generalize tracking and disturbance rejection guarantees for NL systems. But as we show, such guarantees are not true in general. We propose to solve this problem by the application of incremental stability and performance, which does indeed ensure these specifications. In this talk, an overview of the theoretical results is given and stability and performance notions related to incremental stability and dissipativity are presented. Based on these results, a novel approach is proposed to synthesize and realize an LPV controller which is able to guarantee incremental stability and performance for NL systems via convex optimization. Through examples, the presented method is compared to standard L2 gain optimal LPV controller design, showing significant performance improvements. Finally, the approach is experimentally verified on an unbalanced disc setup, displaying the shortcomings of standard L2 gain optimal LPV controllers.
Roland Tóth obtained his BSc in Electrical Engineering and MSc degree in Information Technology cum laude from the University of Pannonia (Hungary) in 2004. In 2008, he completed his PhD in Control Engineering at the Delft University of Technology (TU Delft), also cum laude. From 2008 to 2010, Tóth worked as a postdoctoral researcher at TU Delft while also working on a research project for Philips Apptech. In 2010, he joined the University of California (USA) for a postdoc project, before returning to the Netherlands in 2011 to become Assistant Professor at TU Delft. Tóth joined Eindhoven University of Technology as Assistant Professor in 2012 and was promoted to Associate Professor in 2018. His research interests are in linear parameter-varying (LPV) and nonlinear system identification and control, machine learning for modelling and control, model predictive control and behavioral system theory. Dr. Tóth received the TUDelft Young Researcher Fellowship Award in 2010, the VENI award of The Netherlands Organisation for Scientific Research in 2011 and the Starting Grant of the European Research Council in 2016.