In 2008, Roland Tóth received the Ph.D. degree with cum laude distinction at the Delft Center for Systems andControl (DCSC), Delft University of Technology (TUDelft), Delft, The Netherlands, He was a Post-Doctoral Research Fellow at DCSC, TUDelft in 2009 and at the Berkeley Center for Control and Identification, University of California, Berkeley in 2010. He held a position at DCSC, TUDelft in 2011-12. Currently, he is an Associate Professor at the Control Systems Group, Eindhoven University of Technology (TU/e) and he is a senior researcher at the Systems and Control Laboratory, Institute for Computer Science and Control in Budapest, Hungary. He is an Associate Editor of the IEEE Transactions on Control Systems Technology and he was the general chair of the 3rd IFAC Workshop on Linear Parameter-Varying Systems.
His research interests are in identification and control of linear parameter-varying (LPV) and nonlinear systems, developing machine learning methods with performance and stability guarantees for modelling and control, model predictive control and behavioural system theory. On the application side, his research focuses on advancing reliability and performance of precision mechatronics and autonomous robots/vehicles with LPV and learning-based motion control.
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. He and his research team has participated in several international (FP7, IT2-ESCEL, etc.) and national collaborative research grants.
Tóth, R.: Modeling and identification of linear parameter-varying systems. Lecture Notes in Control and Information Sciences, Vol. 403, Springer, Heidelberg, 2010.
Laurain, V., R. Tóth, D. Piga, M.A.H. Darwish: Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification, Automatica, Vol. 115, (2020).
Cox, P. B., and R. Tóth: Linear Parameter-Varying Subspace Identification: A Unified Framework. Automatica, In Print (2020).
Formentin, S., D. Piga, R. Tóth, S. Savaresi: Direct learning of LPV controllers from data, Automatica, Vol. 65, (2016), pp. 98-110.
Laurain, V., R. Tóth, D. Piga, W. X. Zheng: An Instrumental Least Squares Support Vector Machine for Nonlinear System Identification, Automatica, Vol. 54, (2015), pp 340-347.
